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Modern analytical systems must process streaming data and correctly respond to data distribution changes. The phenomenon of changes in data distributions is called concept drift , and it may harm the quality of the used models. Additionally, the possibility of concept drift appearance causes that the used algorithms must be ready for the continuous adaptation of the model to the changing data distributions. This work focuses on non-stationary data stream classification, where a classifier ensemble is used. To keep the ensemble model up to date, the new base classifiers are trained on the incoming data blocks and added to the ensemble while, at the same time, outdated models are removed from the ensemble. One of the problems with this type of model is the fast reaction to changes in data distributions. We propose the new Chunk Adaptive Restoration framework that can be adapted to any block-based data stream classification algorithm. The proposed algorithm adjusts the data chunk size in the case of concept drift detection to minimize the impact of the change on the predictive performance of the used model. The experimental research, backed up with the statistical tests, has proven that Chunk Adaptive Restoration significantly reduces the model’s restoration time.
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Journal of Universal Computer Science, vol. 28, no. 3 (2022), 249-268
submitted: 23/10/2021, accepted: 7/12/2021, appeared: 28/3/2022 CC BY-ND 4.0
Employing chunk size adaptation to overcome concept
Jędrzej Kozal
(Wrocław University of Science and Technology, Wrocław, Poland,
Filip Guzy
(Wrocław University of Science and Technology, Wrocław, Poland,
Michał Woźniak
(Wrocław University of Science and Technology, Wrocław, Poland,
Modern analytical systems must process streaming data and correctly respond to data
distribution changes. The phenomenon of changes in data distributions is called concept drift, and it
may harm the quality of the used models. Additionally, the possibility of concept drift appearance
causes that the used algorithms must be ready for the continuous adaptation of the model to the
changing data distributions. This work focuses on non-stationary data stream classification, where
a classifier ensemble is used. To keep the ensemble model up to date, the new base classifiers are
trained on the incoming data blocks and added to the ensemble while, at the same time, outdated
models are removed from the ensemble. One of the problems with this type of model is the fast
reaction to changes in data distributions. We propose the new Chunk Adaptive Restoration frame-
work that can be adapted to any block-based data stream classification algorithm. The proposed
algorithm adjusts the data chunk size in the case of concept drift detection to minimize the impact
of the change on the predictive performance of the used model. The experimental research, backed
up with the statistical tests, has proven that Chunk Adaptive Restoration significantly reduces the
model’s restoration time.
Data stream, Data stream mining, Continual learning, Pattern classification, Concept
drift, Block-based data processing
Categories: H.2.8, I.2.6, I.5.0, I.5.2
DOI: 10.3897/jucs.80735
1 Introduction
Data stream mining focuses on the knowledge extraction from streaming data, mainly
for the predictive model construction aimed at assigning arriving instances to one of the
predefined categories. This process is characterized by additional difficulties caused by
the data distribution evolution It is visible in many practical tasks as spam detection,
where the spammers still change the message format to cheat anti-spam systems. Another
example is medical diagnostics, where new SARS-CoV-2 mutations may cause different
symptoms, which forces doctors to adapt and improve diagnostic methods [Harvey et al.,
250 Kozal J., Guzy F., Wniak M.: Employing chunk size adaptation toovercome concept drift
The phenomenon mentioned above is called concept drift, and its nature can vary
due to both the character and the rapidity. It forces classification models to adapt to
new data characteristics and forget previously known, useless concepts. An important
characteristic of such systems is their reaction to the concept drift phenomenon, i.e., how
much predictive performance deteriorates when it occurs and when the classification
system will obtain the approved predictive quality for the new concept. We should also
consider another limitation: the classification system should be ready to classify incoming
objects immediately, and dedicated computing and memory resources are limited.
Data processing models used by stream data classification systems can be roughly
divided into two categories: online (object by object) processing (online learners) or
block-based (chunk by chunk) data processing (block-based learners) [Krawczyk et al.,
2017]. Online learners require model parameters to be updated when a new object appears,
while the block-based method requires updates once per batch. The advantage of online
learners is their fast adaptation to concept drift. However, in many practical applications,
the effort of necessary computation (related to updating models after processing each
object) is unacceptable. The model update can require many operations that involve
changing data statistics, updating the model’s internal structure, or learning a new model
from scratch. These requirements can become prohibitive for high-velocity streams.
Hence, more popular is block-based data processing, which requires less computational
effort. However, it limits the model’s potential for quick adaptation to changes in data
distribution and fast restoration of performance after concept drift. Consequently, a
significant problem is the proper selection of the chunk size. Smaller data block size
results in faster adaptation. However, it increases the overall computing load. On the other
hand, larger data chunks require less computation but result in a lower adaptive capacity
of the classification model. Another valid consideration is the impact of chunk size on
prediction stability. Models trained on smaller chunks typically have more significant
prediction variance, while models trained with larger chunks tend to have more stable
predictions when the data stream is stationary. If concept drift occurs, a larger chunk
size increases the probability that the data from different concepts will be placed in the
same batch. Hence, selecting the chunk size is a trade-off encompassing computation
power, adaptation speed, and predictions variance.
The trade-off described above includes features that are equally desired in many
applications. Especially both the consumption of computational power and the speed
of adaptation are important when processing large data streams. We propose a new
method that alleviates the downfalls of choosing between small or large chunk sizes
by dynamically changing the current batch size. More precisely, our work introduces
the Chunk-Adaptive Restoration (CAR), a framework based on combined drift and
stabilization detection techniques that adjusts the chunk sizes during the concept drift.
This approach slightly redefines the previous problem based on the observation that for
many practical classification tasks, a period of changes in data distributions is followed
by stabilization. Hence, we propose that when the concept drift occurs, the model should
be quickly upgraded, i.e., the data should be processed in small chunks, and during the
stabilization period, the data block size may be extended. The advantage of the proposed
method is its universality and the possibility of using it with various chunk-based data
stream classifiers.
This work offers the following contributions:
Proposing the Chunk-Adaptive Restoration framework to empower fluent restoration
after concept drift appearance.
Formulating the Variance-based Stabilization Detection Method, a technique com-
Kozal J., Guzy F., Wniak M.: Employing chunk size adaptation toovercome concept drift 251
plementary to all concept drift detectors that simplifies chunk size adaptation and
metrics calculation.
Employing Chunk-Adaptive Restoration for the adaptive data chunk size setting for
selected state-of-the-art algorithms.
Introducing a new stream evaluation metric, Sample Restoration, to show the gains
of the proposed methods.
Evaluating the proposed approach based on various synthetic and real data streams
and a detailed evaluation of its usefulness for the selected state-of-art methods.
2 Related works
This section provides a review of the related works. Firstly, we will discuss challenges
specific to the learning from non-stationary data streams. Next, we discuss different
methods of processing data streams. Following, we describe existing drift detection algo-
rithms and ensemble methods. We continue by reviewing existing evaluation protocols
and computational and memory requirements. We conclude this section by providing
examples of other data stream learning methods that employ variable chunk size.
2.1 Challenges related to data stream mining
A data stream is a sequence of objects described by their attributes. In the case of a
classification task, each learning object should be labeled. The number of items may be
vast, potentially infinite. Observations in the stream may arrive at different times, and
the time intervals between their arrival could vary considerably. The main differences
between analyzing data streams and static datasets include [Bifet et al., 2018]:
No one can control the order of incoming objects.
The computation resources are limited, but the analyzer should be ready to process
the incoming item in a reasonable time.
The memory resources are also limited, but the data stream size may be huge or
even infinite, which causes memorizing all the items impossible.
Data streams are susceptible to change, i.e., data distributions may change over time.
The labels of arriving items are not free, for some cases impossible to get, or available
with delay (e.g., in banking for credit approval task after a few years).
The canonical classifiers usually do not consider that the probabilistic characteristics
of the classification task may evolve [Duda et al., 2001]. Such a phenomenon is known
as concept drift [Widmer and Kubat, 1996], and a few concept drift taxonomies have
been proposed. The most popular consider how rapid the drift is, then we can distinguish
sudden drift and incremental one. An additional difficulty is a case when, during the
transition between two concepts, objects from two different concepts appear for some
time simultaneously (gradual drift). We can also take into consideration the influence of
the probabilistic characteristics on the classification task [Joao et al., 2014]:
252 Kozal J., Guzy F., Wniak M.: Employing chunk size adaptation toovercome concept drift
Virtual concept drift does not impact the decision boundaries but affects the proba-
bility density functions [Widmer and Kubat, 1993], and Widmer and Kubat [Widmer
and Kubat, 1996] imputed it rather to incomplete data representation than to the true
changes in concepts,
Real concept drift affects the posterior probabilities and may impact the uncondi-
tional probability density function [Widmer and Kubat, 1996].
2.2 Methods for processing data streams
The data stream can be divided into small portions of the data called data chunks. This
method is known as batch-based or chunk-based learning. Choosing the proper size of
the chunk is crucial because it may significantly affect the classification [Junsawang
et al., 2019]. Unfortunately, the unpredictable appearance of the concept drift makes
it difficult. Several approaches may help overcome this problem, e.g., using different
windows for processing data [Lazarescu et al., 2004] or adjusting chunk size dynamically
[Widmer and Kubat, 1996]. Unfortunately, most chunk-based classification methods
assume that the size of the data chunk is priorly set and remains unchanged during the
data processing.
Instead of chunk-based learning, the algorithm can learn incrementally (online) as
well. Training examples arrive one by one at a given time and are not kept in memory. The
advantage of this solution is the need for small memory resources. However, the effort of
necessary computation related to updating models after processing each individual object
is unacceptable, especially in the high-velocity data streams, i.e., Internet of Things (IoT)
When processing a non-stationary data stream, we can rely on a drift detector to
point moments when data distribution has changed and take appropriate actions. The
alternative is to use inherent adaptation properties of models (update & forget). In the
following subsection, we will discuss both of these approaches.
2.3 Drift detection methods
A drift detector is an algorithm that can inform any changes within data stream distri-
butions. The data labels or a classifier’s performance (measured using any metric, such
as accuracy) is required to detect a real concept drift [Sobolewski and Wozniak, 2013].
We have to realize that drift detection is a non-trivial task. The detection should be done
as quickly as possible to replace an outdated model and minimize restoration time. On
the other hand, false alarms are unacceptable, as they will lead to an incorrect model
adaptation and resource spending where there is no need for it [Gustafsson, 2000]. Drift
Detection Method (DDM) [Gama et al., 2004] is one of the most popular detectors that
incrementally estimates an error of a classifier. Because we assume the classifier training
method’s convergence, the error should decrease with the appearance of subsequent
learning objects [Raudys, 2014]. If the reverse behavior is observed, we may suspect
a change of probability distributions. DDM uses the three-sigma rule to detect a drift.
Early Drift Detection Method (EDDM) [Baena-Garcıa et al., 2006] is an extension of
DDM, where the window size selection procedure is based on the same heuristics. Addi-
tionally, the distance error rate is used instead of the classifier’s error rate. Blanco et al.
[Blanco et al., 2015] proposed very interesting drift detectors that use the non-parametric
estimation of classifier error employing Hoeffding’s and McDiarmid’s inequalities.
Kozal J., Guzy F., Wniak M.: Employing chunk size adaptation toovercome concept drift 253
2.4 Ensemble methods
One of the most promising data stream classification research directions, which usually
employs chunk-based data processing, is the classifier ensemble approach [Krawczyk
et al., 2017]. Its advantage is that the classifier ensemble can easily adapt to the concept
drift using different updating strategies [Kuncheva, 2004]:
Dynamic combiners – individual classifiers are trained in advance, and they are not
updated anymore. The ensemble classifier adapts to changing data distribution by
changing the combination rule parameters.
Updating training data – incoming examples are used to retrain component classifiers
(e.g., online bagging [Oza and Tumer, 2008]).
Updating ensemble members [Bifet et al., 2009, Rodríguez and Kuncheva, 2008].
Changing ensemble lineup – replacing outdated classifiers in the ensemble, e.g., new
individual models are trained on the most recent data and added to the ensemble.
The ensemble pruning procedure is applied, which chooses the most valuable set of
individual classifiers [Jackowski, 2014].
A comprehensive overview of classifier ensemble techniques was presented by
Krawczyk et al. [Krawczyk et al., 2017]. Let us shortly characterize some popular
strategies used during the experiments. Streaming Ensemble Algorithm (SEA) [Street and
Kim, 2001] is the simple classifier ensemble with changing lineup, where the individual
classifiers are trained on the successive data chunks. The base classifiers with the lowest
accuracy are removed from the ensemble to keep the model up-to-date. Wang et al.
proposed Accuracy Weighted Ensembles (AWE) [Woźniak et al., 2013] employing the
weighted voting rules, where weights depend on the accuracy obtained on the testing data.
Brzezinski and Stefanowski proposed Accuracy Updated Ensemble (AUE), extending
AWE by using online classifiers and updating them according to the current distribution
[Brzeziński and Stefanowski, 2011]. Wozniak et al. developed Weighted Aging Ensemble
(WAE), which trains base classifiers on successive data chunks, and the final decision is
made on weighted voting, where weights depend on accuracy and ensemble diversity.
This algorithm additionally employs the decoy function to decrease the weights of
outdated individuals [Woźniak et al., 2013].
2.5 Existing evaluation methodology
Because this work mainly focuses on improving classifier behavior after the concept
drift appearance, apart from the classifier’s predictive performance, we should also
consider memory consumption, the time required to update the model, and time to decide.
However, it should also be possible to evaluate how the model reacts to changes in the
data distribution. Shaker and Hüllermeier [Shaker and Hüllermeier, 2015] presented a
complete framework for evaluating the recovery rate, including the proposition of two
metrics restoration time and maximum performance loss. In this framework, the notion
of pure streams was introduced, i.e., streams containing only one concept. Two pure
are mixed into third stream
, starting with concepts only from the
first stream and gradually increasing a percentage of concepts from the second stream.
Restoration time was defined as a length of the time interval between two events - first,
a performance measured on
drops below 95% of a
performance, and then the
254 Kozal J., Guzy F., Wniak M.: Employing chunk size adaptation toovercome concept drift
performance on
rise above 95% of
performance. The Maximum performance loss
is the maximum difference between
performance and lowest performance on either
. Zliobaite et al. [Zliobaite et al., 2015] proposed that evaluating the profit from
the model update should consider the memory and computing resources involved in its
2.6 Computational and memory requirements
While designing a data stream classifier, we should also consider the computation power
and memory limitations and that we usually have limited access to data labels. These
data stream characteristics pose the need for other algorithms than ones previously
developed for batch learning, where data are stored infinitely and persistently. Such
learning algorithms cannot fulfill all data stream requirements, such as memory usage
constraints, limited processing time, and one scan of incoming examples. However,
simple incremental learning is usually insufficient, as it does not meet tight computational
demands and does not tackle evolving nature of data sources [Krempl et al., 2014].
Constraints on memory and time have resulted in different windowing techniques,
sampling (e.g., reservoir sampling), and other summarization approaches. Also, we have
to realize that when the concept drift appears, data from the past may become irrelevant
or even harmful for the current models, deteriorating the predictive performance of the
classifiers. Thus an appropriate implementation of a forgetting mechanism (where old
data instances are discarded) is crucial.
2.7 Other approaches that modify chunk size
Dynamic chunk size adaptation has been considered in the previous works.Liu et al.
[Liu et al., 2017] utilize information about the occurrence of drift from drift detector. If
drift occurs in the middle of the chunk, data is divided into two chunks, hence dynamic
chunk size. If there is no drift inside the chunk, the whole batch is used. In the prepared
chunk, the majority class is undersampled. A new classifier is trained and added to
the ensemble, and older classifiers are updated. Lu et al. [Lu et al., 2020] also utilize
an ensemble framework for imbalanced stream learning. In this approach, chunk size
grows incrementally. Two chunks are compared based on ensembles predictions variance.
An algorithm for calculating prediction variance called subunderbagging is introduced.
Computed variance is compared using F-test. Chunk size increases if the p-value is less
than a predefined threshold; otherwise, the whole ensemble is updated with the selected
chunk size. The whole process repeats as long as the p-value is lower than the threshold.
In both of these works, dynamic chunk size was used to handle imbalanced data streams.
In contrast, we show that changing chunk size can be beneficial when handling concept
drifts in general. Therefore, we do not focus primarily on imbalanced data.
Bifet et al. [Bifet and Gavaldà, 2007] introduced a method for handling concept drit
with varying chunk sizes. Each incoming chunk is divided into two parts: older and new.
Empirical means of data in each subchunk are compared using Hoeffding bound. If the
difference between two means exceeds the threshold defined by confidence value, then
data in the older window is qualified as out of date and is dropped. Later window with
data for current concept grows, until next drift is detected and data is split again. This
approach allows for detecting drift inside the chunk.
Kozal J., Guzy F., Wniak M.: Employing chunk size adaptation toovercome concept drift 255
3 Methods
This paper presents a general framework that can be used for training any chunk-based
classifier ensemble. This approach aims to reduce the restoration time, i.e., a period
needed to stabilize the classification model performance after concept drit occurs. As we
mentioned, most methods assume a fixed data chunk size, which is a parameter of these
algorithms. Our proposal does not modify the core of a learning algorithm itself. Still,
based on the predictive performance estimated on a given data chunk, it only indicates
what data chunk size is to be taken by a given algorithm in the next step. We provide
the schema of our method in Fig. 1. The intuition tells us that after the occurrence of
the concept drit, the size of the chunk should be small to quickly train new models that
will replace the models learned on the data from the previous concept in the ensemble.
When the stabilization is reached, the ensemble contains base models trained on data
from a new concept. At this moment, we can extend the chunk size so classifiers in the
ensemble can achieve better performance and even greater stability by learning on larger
portions of data from the streams because the analyzed concept is already stable.
Figure 1: Chunk-Adaptive Restoration visualization. Red line marks the concept drit,
green line marks the stabilization.
Let us present the proposed framework in detail.
3.1 Chunk-Adaptive Restoration
Starting the learning process, we sample the data from the stream with a constant chunk
and monitor the classifier performance using a concept drit detector to detect
changes in data distribution. When the drift occurs, we decrease the chunk size to the
smaller value
, i.e.,
is the predefined size of a batch for concept drit. Size of
subsequent chunks after drift at given time
are computed using the following equation:
ct=min(bαct1c, c)(1)
α > 1
. The chunk size grows continuously with each step to reach the original
unless the stabilization is detected. Then the chunk size is set to
immediately. Let
us introduce the Variance-based Stabilization Detection Method (VSDM) to detect the
predictive performance stabilization. First, we define the fixed-sized sliding window
containing the last
predictive performance metric values obtained for the most recent
chunks. We also introduce the stabilization threshold
. The stabilization is detected
when the following condition is met:
V ar(W)< s(2)
256 Kozal J., Guzy F., Wniak M.: Employing chunk size adaptation toovercome concept drift
V ar(W)
is a variance of scores obtained for the last
chunks. Sample data
stream with detected drift and stabilization is presented in Fig. 2. The primary assumption
of the proposed method is a faster model adaptation caused by the increased number of
updates after a concept drift. This strategy allows for using the larger chunk sizes when
the data is not changing. It also reduces the computational costs of retraining models.
Alg. 1 present the whole procedure. Our method works with existing models for online
learning. For this reason, we argue that the approach proposed in this paper is easier to
deploy in practice.
Figure 2: Exemplary accuracy for data stream with abrupt concept. Red line denotes
drift detection, green stabilization detection, and blue beginning of a real drift.
3.2 Memory and time complexity
Our method only impacts the size of the chunk. All other factors like the number of
features or classifiers in the ensemble are the same as in the basic approach. For this
reason, we will focus here only on the impact of chunk size on memory and time
complexity. With memory complexity, our method could impact only the size of buffers
for storing samples from a stream. When no drift is detected, the standard chunk size
is used. This dictates the required size of buffers for storing samples. For this reason,
memory complexity for storing samples is O(c).
CAR works the same way as a base method when no drift is detected and the data
stream is stable. Therefore, in this case, the time complexity is the same as in the base
method. When drift is detected sizes of subsequent chunks are changed. Time complexity
depends on model complexity
, where
is the number of learning examples
provided to model to train on. For simplicity, we assume that
represents both
ensemble and base model complexity. With this assumptions time complexity of base
model (when CAR is not enabled) is:
. When CAR is enabled, and concept drit
is detected, the chunk size is changed to
. Each consecutive chunk at time
has size
Kozal J., Guzy F., Wniak M.: Employing chunk size adaptation toovercome concept drift 257
Algorithm 1 Chunk-Adaptive Restoration algorithm
Input: m- model
S- data stream
dd - drift detector
sd - stabilization detector
n- number of chunks
t- chunk index
c- base chunk size
cd- base drift chunk size
ct-tth chunk size
test() - procedure that tests model with a chunk and returns the predictive performance
metric (ppm)
train() - procedure that trains model with a chunk
change_detected() - procedure that informs about drift occurrence with the drift
detector and the last score
stabilization_detected() - procedure that detects stabilization with the stabilization
detector and the stabilization window
1: for t= 1 to ndo
2: ppm test(m, S(t))
3: if stabilization_detected(sd, ppm)then
4: ctc
5: else
6: ctmin(bαct1c, c)
7: end if
8: if change_detected(dd, ppm)then
9: ctcd
10: end if
11: train(m, S(t))
12: end for
, with
t= 0
directly after the drift was detected. Chunk size grows until
stabilization is detected or current chunk size is restored to original size
. For simplicity,
we skip the case when stabilization is detected. With this assumption, we write condition
for restoring the original chunk size:
is the time when the chunk size is restored to its original value. From this
equation, we obtain tsdirectly:
258 Kozal J., Guzy F., Wniak M.: Employing chunk size adaptation toovercome concept drift
The number of operations required by CAR after concept drit was detected is
Using big-O notation:
g(αtcd)) = O(g(αtscd)) = O(g(c
cd)) = O(g(c)) (6)
Therefore CAR time complexity depends only on chunk size and computational
complexity of used models.
3.3 Sample Restoration
Restoration time cannot be directly utilized in this work, as we do not have access to pure
streams with separate concepts. For this reason, we introduce a new Sample Restoration
(SR) metric to evaluate the Chunk-Adaptive Restoration performance compared to stan-
dard methods used for learning models on data streams with concept drit. We assume
that there is a sequence of
chunks between two stabilization points. Each element of
such a sequence is determined by the chunk size
and the achieved model’s accuracy
acct. Let us define the index of the minimum accuracy as:
tmin =argmin
and the restoration threshold is given by the following formula:
t[tmin,N )acct(8)
is the percentage of the performance that has to be restored, and the
multiplier is the maximum accuracy score of our model after the point when it achieved
its minimum score. Finally, we look for the lowest index
after which the model exceeds
the assumed restoration threshold:
t[tmin,N ){t:acctr}(9)
Sample Restoration is computed as the sum of chunk sizes from the concept drit’s
beginning to the tr:
SR(p) =
In general, SR is the number of samples needed to obtain the
percent of the maximum
performance achieved on the subsequent task.
4 Experiment
Chunk-Adaptive Restoration is a method designed to reduce the number of samples used
to restore the model’s performance during the concept drift. We expect to significantly
Kozal J., Guzy F., Wniak M.: Employing chunk size adaptation toovercome concept drift 259
reduce the Sample Restoration for each trained model depending on the chunk size
adaptation level. The experimental study was formulated to answer the following research
RQ1: How do different chunk sizes impact predictive performance?
RQ2: How does the Chunk-Adaptive Restoration influence the learning process?
RQ3: How many samples can be saved during the restoration phase?
RQ4: How do different classifier ensemble models behave with the application of
Chunk-Adaptive Restoration?
RQ5: How robust to noise the Chunk-Adaptive Restoration is?
4.1 Experiment setup
Data streams.
Experiments were carried out using both synthetic and real datasets.
Stream-learn library [Ksieniewicz and Zyblewski, 2020] was employed to generate the
synthetic data containing three types of concept drift: abrupt, gradual, and increment, all
generated with the recurring or unique concepts. We tested parameters such as chunk
sizes and the stream length for each type of concept drift. All streams were generated
with 5 concept drits, 2 classes, 20 input features, of which 2 were informative, and 2
were redundant. In the case of incremental and gradual drifts concept, sigmoid spacing
was set to 5. Apart from the synthetic ones, we employed the Usenet [Katakis et al.,
2010] and Insects [Souza et al., 2020] data streams. Unfortunately, the original Usenet
dataset contains a small number of samples, so two selected concepts were repeated
to create a recurring-drifted data stream. Each chunk of the Insects data stream was
randomly oversampled because of the significant imbalance ratio. Tab. 1 contains detailed
description of all utilized data streams.
Drift detector.
The Fast Hoeffding Drift Detection Method [Blanco et al., 2015] was
employed as a concept drit detector. We used implementation available on the public
repository [w4k2, 2021]. The size of a window in FHDDM was equal to 1000, and the
error probability allowed δ= 106.
Classifier ensembles.
Three models classifier ensembles dedicated to data stream clas-
sification were chosen for comparison:
Weighted Aging Classifier (WAE) [Woźniak et al., 2013]
Accuracy Weighted Ensemble (AWE) [Wang et al., 2003],
Streaming Ensemble Algorithm (SEA) [Street and Kim, 2001],
All ensembles contained 10 base classifiers.
Experimental protocol.
In our experiments, we apply the models mentioned above
to selected data streams with concept drit. We measure Sample Restoration. These
results are reported as a baseline. Next, we apply Chunk-Adaptive Restoration and repeat
experiments to establish the proposed model’s influence on the ability to handle concept
drit quickly. As the experiments were conducted with the balanced data, the accuracy
was used as the only indicator of the model’s performance. As the experimental protocol,
Test-Then-Train was employed [Bifet et al., 2010].
260 Kozal J., Guzy F., Wniak M.: Employing chunk size adaptation toovercome concept drift
# Source Drift type
size c
1 stream-learn abrupt recurring 500 300000
2 stream-learn abrupt recurring 1000 150000
3 stream-learn abrupt recurring 10000 60000
4 stream-learn abrupt recurring 500 250000
5 stream-learn abrupt nonrecurring 500 300000
6 stream-learn abrupt nonrecurring 1000 150000
7 stream-learn abrupt nonrecurring 10000 60000
8 stream-learn abrupt nonrecurring 500 250000
9 stream-learn gradual recurring 500 300000
10 stream-learn gradual recurring 1000 150000
11 stream-learn gradual recurring 10000 60000
12 stream-learn gradual recurring 500 250000
13 stream-learn gradual nonrecurring 500 300000
14 stream-learn gradual nonrecurring 1000 150000
15 stream-learn gradual nonrecurring 10000 60000
16 stream-learn gradual nonrecurring 500 250000
17 stream-learn incremental recurring 500 300000
18 stream-learn incremental recurring 1000 150000
19 stream-learn incremental recurring 10000 60000
20 stream-learn incremental recurring 500 250000
21 stream-learn incremental nonrecurring 500 300000
22 stream-learn incremental nonrecurring 1000 150000
23 stream-learn incremental nonrecurring 10000 60000
24 stream-learn incremental nonrecurring 500 250000
25 usenet abrupt recurring 1000 120000
26 insects-abrupt-imbalanced abrupt nonrecurring 1000 355275
27 insects-gradual-imbalanced gradual nonrecurring 1000 143323
Table 1: Data streams used for experiments.
Statistical analysis.
Because Sample Restoration can be computed for each drift and
concept drit can occur multiple times, we report average Sample Restoration for each
stream with standard deviation. To assess the statistical significance of the results, we
used a one-sided Wilcoxon signed-rank test in a direct comparison between the models
with the 95% confidence level.
To enable independent reproduction of our experiments, we provide
a GitHub repository with code
. This repository also contains detailed results of all
experiments. Stream-learn [Ksieniewicz and Zyblewski, 2020] implementation of the
ensemble models was utilized with the Gaussian Naïve Bayes and CART as base classi-
fiers taken from the Scikit Learn library[Pedregosa et al., 2011]. Detailed information
about used packages is provided in the YAML file with a specification of the Anaconda
Kozal J., Guzy F., Wniak M.: Employing chunk size adaptation toovercome concept drift 261
4.2 Impact of chunk size on performance
In our first experiment, we examine the impact of the chunk size on the model performance
and general capability for handling data with concept drit. To evaluate these properties,
we train the AWE model on a synthetic data stream with different chunk sizes. The
stream consists of 20 features, 2 classes, and it contains only 1 abrupt drift. Results are
presented in Fig. 3. As expected, chunk size has an impact on the maximal accuracy that
the model can achieve. It is especially visible before a drift, where models with larger
chunks obtain the best accuracy. Also, with larger chunks variance of accuracy is lower.
In ensemble-based approaches, a base classifier is trained on a single chunk. A larger
chunk means that more data is available to the underlying model. Therefore it allows for
the training of a more accurate model. Interestingly we can see that for all chunk sizes,
performance is restored roughly at the same time. Regardless of the chunk size, a similar
number of updates is required to bring back the model performance. Please keep in mind
that the x-axis in Fig. 3 is the number of chunks. It means that models trained on larger
chunks require a larger number of learning examples to restore accuracy.
Figure 3: Impact of chunk size on obtained accuracy.
These results give the rationale behind our method. When drift is detected, we change
chunk size to decrease the consumption of learning examples required for restoring
accuracy. Next, we gradually increase chunk size to improve the maximum possible
performance when the model recovers from drift. It allows for a quick reaction to drift
and does not limit the model’s maximum performance. In principle, not all models are
compatible with changing chunk size. Also, batch size cannot be decreased indefinitely.
Minimal chunk size should be determined case by case, dependent on the base learner
used in an ensemble or used model in general. Later in our experiments, we use chunk
sizes of 500, 1000, and 10000 to obtain a reliable estimate of how our method will
perform in different settings.
262 Kozal J., Guzy F., Wniak M.: Employing chunk size adaptation toovercome concept drift
4.3 Hyperparameter tuning
After chunk size was selected, we fine-tuned other hyperparameters, and then we pro-
ceeded to further experiments. Firstly set two values manually, based on our observations.
First is
(i.e., constant that determines how fast chunk size grows after drift was detected)
equal to
. Second is drift chunk size equal to 30, as it is a typical window length in
drift detectors.
Next, we find the best stabilization window size and the threshold. We conduct grid
search with windows size values 30, 50, 100, and stabilization thresholds 0.1, 0.01, 0.001,
0.0001. For experiments, we use synthetic data streams 1-24 from the Tab. 1. Used data
streams have different random number generator seeds in this and later experiments.
Results were collected for WAE, AWE, SEA ensembles with Naïve Bayes base model.
We use Sample Restoration 0.8 as a performance indicator. For each set of parameters,
Sample Restoration was averaged over all streams used to obtain one value. Results are
provided in the Tab. 2.
drift chunk size
30 50 100
0.1 59210.11 59210.11 59210.11
0.01 58489.47 58675.99 58709.98
0.001 55328.20 55363.95 57669.70
0.0001 52846.04 55962.58 62398.56
Table 2: Sample Restoration 0.8 for various hyperparameter setting. Lower is better.
From provided data, we can conclude that the smaller the drift chunk size, the lower
the SR is. This observation is in line with intuition about our method. Smaller drift chunk
size provides a larger benefit during drift compared to normal chunk size. The same
dependency can be observed for the stabilization threshold. Intuitively, a lower threshold
means that stabilization is harder to reach. We argue that this can be beneficial in some
cases when working with gradual or incremental drift. In this scenario, if stabilization is
reached too fast, then chunk size is immediately brought back to the standard size, and
there is no benefit from a smaller chunk size at all. Lowering the stabilization threshold
could help in these cases. In later experiments, we use the stabilization window size
equal to 30 and the variance stabilization threshold equal to 0.0001.
4.4 Impact on concept drit handling capability
In this part of the experiments, we compare the performance of the proposed method to
baseline. Results were collected following the experimental protocol described in the
previous sections. To save space, we do not provide results for all models and streams.
Instead, we plot accuracy achieved by models on selected data streams. These results
are presented in Fig. 4, 5, 6, and 7. All learning curves were smoothed using a 1D
Gaussian filter with σ= 1.
From provided plots, we can deduce that the largest gains from employing the
CAR method can be observed for an abrupt data stream. In streams with gradual and
incremental drifts, there are fewer or none sudden drops of accuracy that the model
can quickly react to. For this reason, the CAR method does not provide a large benefit
Kozal J., Guzy F., Wniak M.: Employing chunk size adaptation toovercome concept drift 263
with this kind of concept drits. During a more detailed analysis of obtained results, we
observed that the stabilization for gradual and incremental drifts is hard to detect. Many
false positives usually cause an early return to the original chunk size, influencing the
performance achieved on those two types of drifts. FHDDM caused another problem
regarding the early detection of the gradual and incremental concept drits. Usually, this
is a desired feature. In our method, early drift detection initiates the chunk size change
when two data concepts are still overlapping during stream processing. As the transition
between two concepts takes much time, when one concept starts to dominate, the chunk
size could be restored to its original value too early, affecting the achieved results.
We also observe larger gains from applying CAR on streams with bigger chunk
sizes. To illustrate please compare results from Fig. 4 to Fig. 5. One possible explanation
behind this trend is that gains obtained from employing CAR are proportional to the
difference in size between the base and drift chunk size. In our experiments, drift chunk
size was equal to 30 for all streams and models. This explanation is also in line with the
results of hyperparameter experiments provided in the Tab. 2.
We conclude this section by providing a statistical analysis of our results. Tab. 3
shows the results of the Wilcoxon test for Naïve Bayes and CART base models. We state
meaningful differences in the Sample Restoration between the baseline and the CAR
method for all models.
Figure 4: Accuracy for stream-learn data
stream (1).
Figure 5: Accuracy for Usenet dataset
4.5 Impact of noise on the CAR effectiveness
Real-world data often contain noise in labeling. For this reason, we evaluate if the
proposed method can be used for data with varying amounts of noise in labels. We
generate a synthetic data stream with two classes, base chunk size 1000, drift chunk
size 100, and single, abrupt concept drit. We randomly select a predefined fraction of
samples in each chunk and flip labels for selected learning examples. Next, we measure
the accuracy of the AUE model with Gaussian Naïve Bayes base model on a generated
264 Kozal J., Guzy F., Wniak M.: Employing chunk size adaptation toovercome concept drift
Figure 6: Accuracy for abrupt Insects
dataset (26).
Figure 7: Accuracy for gradual Insects
dataset (27).
Naïve Bayes
SR(0.9) SR(0.8) SR(0.7)
Statistic p-value Statistic p-value Statistic p-value
WAE 40.0 0.0006 30.0 0.0002 45.0 0.0009
AWE 22.0 9.675e-05 26.0 0.0001 36.0 0.0004
SEA 0.0 1.821e-05 23.0 0.0001 1.0 1.389e-05
SR(0.9) SR(0.8) SR(0.7)
Statistic p-value Statistic p-value Statistic p-value
WAE 14.0 6.450e-05 54.0 0.003 55.0 0.003
AWE 0.0 1.229e-05 6.0 2.543e-05 21.0 0.0001
SEA 23.0 0.0001 43.0 0.001 42.0 0.001
Table 3: Wilcoxon test results
dataset with noise levels 0, 0.1, 0.2, 0.3, and 0.4. Results are presented in Fig. 8. We
note that for low noise levels, i.e., up to 0.3, restoration time is shorter. With a larger
amount of noise, there is no sudden drop in accuracy. Therefore CAR has no impact on
the speed of reaction to drift.
It should be noted that results for CAR with noise levels 0.2, 0.3, and 0.4 were
generated with the stabilization detector turned off. With a higher amount of noise,
stabilization was detected very fast. Therefore chunk size was quickly set to base value.
In this case, there was no benefit of applying CAR. This indicates that the stabilization
method should be refined to handle noisy data well.
4.6 Lessons learned
Firstly we evaluated the impact of chunk size on the process of learning in the data stream
with single concept drit. We learn that models with larger chunk size can obtain larger
Kozal J., Guzy F., Wniak M.: Employing chunk size adaptation toovercome concept drift 265
Figure 8: Impact of noise in labels on proposed method effectiveness. (Upper) baseline
accuracy for synthetic data stream with different noise level added to labels. (Lower)
CAR accuracy for the same synthetic data stream. In case of Noise levels 0.2, 0.3, and
0.4 stabilization detector was turned off.
maximum accuracy, but the required number of updates to restore accuracy is similar
regardless of chunk size (RQ1 answered). The main goal of introducing the Chunk-
Adaptive Restoration was to prove its advantages in controlling the number of samples
during the restoration period while dealing with abrupt concept drift. The statistical tests
have shown a significant benefit of employing it in different stream learning scenarios
(RQ2 answered). TThe method’s highest gains were observed when the large original
chunk size was used. There are fewer model updates with a bigger chunk size, resulting
in a delay of reaction to concept drit.
The number of samples that can be saved depends on the drift type and the original
chunk size. When dealing with abrupt drift, the sample restoration time can be around 50%
better than the baseline (RQ3 answered). We noticed that CAR minimized restoration
time for each of the analyzed classifier ensemble methods and achieved better average
predictive performance. It is worth noting that the simpler the algorithm, the greater
the profit from using CAR. The most considerable profit was observed for SEA and
AWE, while in the case of WAE, sometimes the native version outperformed CAR
for the Average Sample Restoration metric (RQ4 answered). When a small amount of
noise is present in labels, CAR can still be useful, however in some cases stabilization
detector should not be used. With a larger amount of noise, there is no gain from using
the proposed method (RQ5 answered).
5 Conclusion
The work focused on the Chunk-Adaptive Restoration framework, which is dedicated
to chunk-based data stream classifiers enabling better recovery from concept drits. We
266 Kozal J., Guzy F., Wniak M.: Employing chunk size adaptation toovercome concept drift
proposed new methods for stabilization detection and chunk size adaptation. It is worth
emphasizing that any block-based data stream classifier can use CAR, especially for the
tasks where concept drit may appear. We evaluated the developed algorithms based on
extensive experimental studies using real and synthetic data streams. Obtained results
show a significant difference between the predictive performance of the baseline models
and models employing CAR. Chunk-Adaptive Restoration is strongly recommended
for abrupt concept drit scenarios because it significantly can reduce model downtime.
The performance gain is not visible for other types of concept drit, but it still achieves
acceptable results. The future works may focus on:
Improving the Chunk-Adaptive Restoration behavior for gradual and incremental
concept drits.
Adapting the Chunk-Adaptive Restoration to the case of limited access to labels
using a semi-supervised and active learning approach.
Proposing a more flexible method of changing data chunk size, e.g., based on the
model stability assessment.
Adapting the proposed method to imbalanced data stream classification task, where
changing the data chunk size may be correlated with the intensity of data prepro-
cessing (e.g., the intensity of data oversampling).
Improve stabilization method to better handle data streams with label and attribute
This work is supported by the CEUS-UNISONO programme, with funding from the
National Science Centre, Poland under grant agreement No. 2020/02/Y/ST6/00037.
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... In works of literature recently, concept drift is further described. At a certain moment, p(x t , y t ) can be obtained from the conditional class concept distribution by formula (2). ...
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The concept drift detection method is an online learner. Its main task is to determine the position of drifts in the data stream, so as to reset the classifier after detecting the drift to improve the learning performance, which is very important in practical applications such as user interest prediction or financial transaction fraud detection. Aiming at the inability of existing drift detection methods to balance the detection delay, false positives, false negatives, and space–time efficiency, a new level transition threshold parameter is proposed, and a multi-level weighted mechanism including "Stable Level-Warning Level-Drift Level" is innovatively introduced in the concept drift detection. The instances in the window are weighted in levels, and the double sliding window is also applied. Based on this, a multi-level weighted drift detection method (MWDDM) is proposed. In particular, two variants which are MWDDM_H and MWDDM_M are proposed based on Hoeffding inequality and Mcdiarmid inequality, respectively. Experiments on artificial datasets show that MWDDM_H and MWDDM_M can detect abrupt and gradual concept drift faster than other comparison algorithms while maintaining a low false positive ratio and false negative ratio. Experiments on real-world datasets show that MWDDM has the highest classification accuracy in most cases while maintaining good space time efficiency.
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We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time. We use sliding windows whose size, instead of being fixed a priori, is recomputed online according to the rate of change observed from the data in the window itself. This delivers the user or programmer from having to guess a time-scale for change. Contrary to many related works, we provide rigorous guarantees of performance, as bounds on the rates of false positives and false negatives. Using ideas from data stream algorithmics, we develop a time-and memory-efficient version of this algorithm, called ADWIN2. We show how to combine ADWIN2 with the Naïve Bayes (NB) predictor, in two ways: one, using it to monitor the error rate of the current model and declare when revision is necessary and, two, putting it inside the NB predictor to maintain up-to-date estimations of conditional probabilities in the data. We test our approach using synthetic and real data streams and compare them to both fixed-size and variable-size window strategies with good results.
stream-learn is a Python package compatible with scikit-learn and developed for the drifting and imbalanced data stream analysis. Its main component is a stream generator, which allows producing a synthetic data stream that may incorporate each of the three main concept drift types (i.e., sudden, gradual and incremental drift) in their recurring or non-recurring version, as well as static and dynamic class imbalance. The package allows conducting experiments following established evaluation methodologies (i.e., Test-Then-Train and Prequential). Besides, estimators adapted for data stream classification have been implemented, including both simple classifiers and state-of-the-art chunk-based and online classifier ensembles. The package utilises its own implementations of prediction metrics for imbalanced binary classification tasks to improve computational efficiency.
The extension of machine learning methods from static to dynamic environments has received increasing attention in recent years; in particular, a large number of algorithms for learning from so-called data streams has been developed. An important property of dynamic environments is non-stationarity, i.e., the assumption of an underlying data generating process that may change over time. Correspondingly, the ability to properly react to so-called concept change is considered as an important feature of learning algorithms. In this paper, we propose a new type of experimental analysis, called recovery analysis, which is aimed at assessing the ability of a learner to discover a concept change quickly, and to take appropriate measures to maintain the quality and generalization performance of the model. We develop recovery analysis for two types of supervised learning problems, namely classification and regression. Moreover, as a practical application, we make use of recovery analysis in order to compare model-based and instance-based approaches to learning on data streams.
This paper presents a novel ensemble classifier system designed to process data streams featuring occasional changes in their characteristics (concept drift). The ensemble is especially effective when the concepts reappear (recurring context). The system collects information on emerging contexts in a pool of elementary classifiers trained on subsequent data chunks. The pool is updated only when concept drift is detected. In contrast to other ensemble solutions, classifiers are not removed from the pool, and therefore, knowledge of past contexts is preserved for future use. To ensure high classification performance, the number of classifiers contributing to decision-making is fixed and limited. Only selected elements from the pool can join the decision-making ensemble. The process of selecting classifiers and adjusting their weights is realized by an evolutionary-based optimization algorithm that aims to minimize the system misclassification rate. Performance of the system is evaluated through a series of experiments presenting some key features of the system.
Early drift detection method
  • Baena-Garcıa
Baena-Garcıa et al., 2006] Baena-Garcıa, M., del Campo-Ávila, J., Fidalgo, R., Bifet, A., Gavalda, R., and Morales-Bueno, R. (2006). Early drift detection method. In Fourth international workshop on knowledge discovery from data streams, volume 6, pages 77-86.
Statistical and Neural Classifiers: An Integrated Approach to Design
  • S Raudys
  • J J Rodríguez
  • L I Kuncheva
[Raudys, 2014] Raudys, S. (2014). Statistical and Neural Classifiers: An Integrated Approach to Design. Springer Publishing Company, Incorporated. [Rodríguez and Kuncheva, 2008] Rodríguez, J. J. and Kuncheva, L. I. (2008). Combining online classification approaches for changing environments. In Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, SSPR & SPR '08, pages 520-529, Berlin, Heidelberg. Springer-Verlag.