Paweł Ksieniewicz

Paweł Ksieniewicz
Wroclaw University of Science and Technology | WUT · Department of Systems and Computer Networks

PhD

About

51
Publications
6,588
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282
Citations

Publications

Publications (51)
Preprint
Full-text available
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 to produce 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...
Article
Due to variety of modern real-life tasks, where analyzed data is often not a static set, the data stream mining gained a substantial focus of machine learning community. Main property of such systems is the large amount of data arriving in a sequential manner, which creates an endless stream of objects. Taking into consideration the limited resourc...
Article
Full-text available
The classification of data stream susceptible to the concept drift phenomenon has been a field of intensive research for many years. One of the dominant strategies of the proposed solutions is the application of classifier ensembles with the member classifiers validated on their actual prediction quality. This paper is a proposal of a new ensemble...
Preprint
Full-text available
The classification problem's complexity assessment is an essential element of many topics in the supervised learning domain. It plays a significant role in meta-learning -- becoming the basis for determining meta-attributes or multi-criteria optimization -- allowing the evaluation of the training set resampling without needing to rebuild the recogn...
Article
Full-text available
Among the difficulties being considered in data stream processing, a particularly interesting one is the phenomenon of concept drift. Methods of concept drift detection are frequently used to eliminate the negative impact on the quality of classification in the environment of evolving concepts. This article proposes Statistical Drift Detection Ense...
Preprint
Full-text available
The abundance of information in digital media, which in today's world is the main source of knowledge about current events for the masses, makes it possible to spread disinformation on a larger scale than ever before. Consequently, there is a need to develop novel fake news detection approaches capable of adapting to changing factual contexts and g...
Article
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...
Article
The latest trends in computer networks bring new challenges and complex optimization problems, one of which is link dimensioning in Spectrally-Spatially Flexible Optical Networks. The time-consuming calculations related to determining the objective function representing the amount of accepted traffic require heuristics to search for good quality so...
Preprint
Full-text available
One of the significant problems of streaming data classification is the occurrence of concept drift, consisting of the change of probabilistic characteristics of the classification task. This phenomenon destabilizes the performance of the classification model and seriously degrades its quality. An appropriate strategy counteracting this phenomenon...
Article
In this paper, we focus on the efficient dynamic routing in Spectrally-Spatially Flexible Optical Networks (ss-fon) realized using Single-Mode Fiber Bundles (smfbs). We study two scenarios – unprotected network (np) and network protected by dedicated path protection (dpp) against a single link failure. For these configurations, we propose a dedicat...
Article
Ensembles of classifiers deserve attention because their stability and accuracy are usually superior compared to the single classifier. One of the aspects regarding the construction of multiple classifier systems is the fusion of each base model output. The state-of-the-art fusion of base classifiers approaches uses class labels, a rank array, or a...
Conference Paper
Despite the fact that real-life data streams may often be characterized by the dynamic changes in the prior class probabilities, there is a scarcity of articles trying to clearly describe and classify this problem as well as suggest new methods dedicated to resolving this issue. The following paper aims to fill this gap by proposing a novel data st...
Chapter
The following work aims to propose a new method of constructing an ensemble of classifiers diversified by the appropriate selection of the problem subspace. The experiments were performed on a numerical dataset in which three groups are present: healthy controls, glaucoma suspects, and glaucoma patients. Overall, it consists of medical records from...
Article
Full-text available
This study aimed to assess the utility of optic nerve head ( onh ) en-face images, captured with scanning laser ophthalmoscopy ( slo ) during standard optical coherence tomography ( oct ) imaging of the posterior segment, and demonstrate the potential of deep learning ( dl ) ensemble method that operates in a low data regime to differentiate glauco...
Article
Many researchers working on classification problems evaluate the quality of developed algorithms based on computer experiments. The conclusions drawn from them are usually supported by the statistical analysis and chosen experimental protocol. Statistical tests are widely used to confirm whether considered methods significantly outperform reference...
Preprint
Full-text available
Fake news has now grown into a big problem for societies and also a major challenge for people fighting disinformation. This phenomenon plagues democratic elections, reputations of individual persons or organizations, and has negatively impacted citizens, (e.g., during the COVID-19 pandemic in the US or Brazil). Hence, developing effective tools to...
Article
Fake news has now grown into a big problem for societies and also a major challenge for people fighting disinformation. This phenomenon plagues democratic elections, reputations of individual persons or organizations, and has negatively impacted citizens, (e.g., during the COVID-19 pandemic in the US or Brazil). Hence, developing effective tools to...
Article
In the diversity of contemporary decision-making tasks, where the data is no longer static and changes over time, data stream processing has become an important issue in the field of pattern recognition. In addition, most of the real problems are not balanced, representing their classes in various improportions. Following paper proposes the Prior I...
Chapter
A significant problem when building classifiers based on data stream is information about the correct label. Most algorithms assume access to this information without any restrictions. Unfortunately, this is not possible in practice because the objects can come very quickly and labeling all of them is impossible, or we have to pay for providing the...
Chapter
Using fake news as a political or economic tool is not new, but the scale of their use is currently alarming, especially on social media. The authors of misinformation try to influence the users' decisions, both in the economic and political sphere. The facts of using disinformation during elections are well known. Currently, two fake news detectio...
Article
Full-text available
In the era of a large number of tools and applications that constantly produce massive amounts of data, their processing and proper classification is becoming both increasingly hard and important. This task is hindered by changing the distribution of data over time, called the concept drift, and the emergence of a problem of disproportion between c...
Chapter
Learning from imbalanced datasets is a challenging task for standard classification algorithms. In general, there are two main approaches to solve the problem of imbalanced data: algorithm-level and data-level solutions. This paper deals with the second approach. In particular, this paper shows a new proposition for calculating the weighted score f...
Chapter
The problem of fake news has become one of the most challenging issues having an impact on societies. Nowadays, false information may spread quickly through social media. In that regard, fake news needs to be detected as fast as possible to avoid negative influence on people who may rely on such information while making important decisions (e.g., p...
Chapter
Many real classification problems are characterized by a strong disturbance in a prior probability, which for the most of classification algorithms leads to favoring majority classes. The action most often used to deal with this problem is oversampling of the minority class by the smote algorithm. Following work proposes to employ a modification of...
Chapter
The following paper considers pattern recognition-aided optimization of complex and relevant problem related to optical networks. For that problem, we propose a four-step dedicated optimization approach that makes use, among others, of a regression method. The main focus of that study is put on the construction of efficient regression model and its...
Article
We focus on optimization of dynamic spectrally-spatially flexible optical networks (SS-FONs), in which distance-adaptive, spectral super-channel (SCh) transmission is realized over weakly-coupled multi-core fibers (MCFs). In such networks, the inter-core crosstalk (XT) effect in MCFs impairs the quality of transmission (QoT) of optical signals, whi...
Conference Paper
Following work focuses on optimization of dynamic spectrally-spatially flexible optical networks aided using supervised learning methods. Such kind of networks have distance-adaptive, spectral super-channel transmission realized over weakly-coupled multi-core fibers. Article proposes employing a pattern recognition approach with the goal to estimat...
Chapter
The problem of the fake news publication is not new and it already has been reported in ancient ages, but it has started having a huge impact especially on social media users. Such false information should be detected as soon as possible to avoid its negative influence on the readers and in some cases on their decisions, e.g., during the election....
Chapter
Imbalanced data classification is still a focus of intense research, due to its ever-growing presence in the real-life decision tasks. In this article, we focus on a classifier ensemble for imbalanced data classification. The ensemble is formed on the basis of the individual classifiers trained on supervise-selected feature subsets. There are sever...
Chapter
Full-text available
In this work we explored capabilities of improving deep learning models performance by reducing the dataset imbalance. For our experiments a highly imbalanced ECG dataset MIT-BIH was used. Multiple approaches were considered. First we introduced mutliclass UMCE, the ensemble designed to deal with imbalanced datasets. Secondly, we studied the impact...
Chapter
From one year to another, more and more vast amounts of data is being created in different fields of application. Great deal of those sources require real-time processing and analyzing, which leads to increased interest in streaming data classification field of machine learning. It is not rare, that many of those applications deal with somehow skew...
Chapter
Full-text available
Following work tries to utilize a hybrid approach of combining Random Subspace method and smote oversampling to solve a problem of imbalanced data classification. Paper contains a proposition of the ensemble diversified using Random Subspace approach, trained with a set oversampled in the context of each reduced subset of features. Algorithm was ev...
Chapter
Full-text available
The nature of analysed data may cause the difficulty of the many practical data mining tasks. This work is focusing on two of the important research topics associated with data analysis, i.e., data stream classification as well as data analysis with imbalanced class distributions. We propose the novel classification method, employing a classifier s...
Chapter
Following paper presents Exposer Ensemble (ee), being a combined classifier based on the original model of quantized subspace class distribution. It presents a method of establishing and processing the Planar Exposer – base representation of discrete class distribution over given subspace, and a proposition how to effectively fuse discriminatory po...
Chapter
The difficulty of the many classification tasks lies in the analyzed data nature, as disproportionate number of examples from different class in a learning set. Ignoring this characteristics causes that canonical classifiers display strongly biased performance on imbalanced datasets. In this work a novel classifier ensemble forming technique for im...
Conference Paper
The big data is usually described by so-called 5Vs (Volume, Velocity, Variety, Veracity, Value). The business success in the big data era strongly depends on the smart analytical software which can help to make efficient decisions (Value for enterprise). Therefore, the decision support software should take into consideration especially that we deal...
Article
Full-text available
Contemporary classification systems have to make a decision not only on the basis of the static data, but on the data in motion as well. Objects being recognized may arrive continuously to a classifier in the form of data stream. Usually, we would like to start exploitation of the classifier as soon as possible, the models which can improve their m...
Conference Paper
For the contemporary enterprises, possibility of appropriate business decision making on the basis of the knowledge hidden in stored data is the critical success factor. Therefore, the decision support software should take into consideration that data usually comes continuously in the form of so-called data stream, but most of the traditional data...
Conference Paper
Remote sensing and hyperspectral data analysis are areas offering wide range of valuable practical applications. However, they generate massive and complex data that is very difficult to be analyzed by a human being. Therefore, methods for efficient data representation and data mining are of high interest to these fields. In this paper we introduce...
Conference Paper
This work reports the research on active learning approach applied to the data stream classification. The chosen characteristics of the proposed frameworks were evaluated on the basis of the wide range of computer experiments carried out on the three benchmark data streams. Obtained results confirmed the usability of proposed method to the data str...
Chapter
Data obtained by hyperspectral imaging gives us enough information to recreate the human vision, and also to extend it by a new methods to extract features coded in a light spectra. This work proposes a set of functions, based on abstraction of natural photoreceptors. The proposed method was employed as the feature extraction for the classification...
Article
Full-text available
The paper concentrates on the problem of limited by the state institutions access to meteorological data, relevant to scientific research, that are regarded only as commercial product. The search for the new source of access was based on the case study of the Baltic Sea area. The aim of the project was to create a meteorological database, independe...
Article
Full-text available
Nowadays, the hyperspectral imaging is the focus of intense research, because its applications can be very useful in the natural disaster monitoring and agricultural monitoring to enumerate only a few. The main problem of systems using hyperspectral imaging is the cost of labelling, because it requires the domain experts, who label the region or pr...
Chapter
This work is focusing on the hyperspectral imaging classification, which is nowadays a focus of intense research. The hyperspectral imaging is widely used in agriculture, mineralogy, or food processing to enumerate only a few important domains. The main problem of such image classification is access to the ground truth, because it needs the experie...
Conference Paper
Hyperspectral image analysis is a dynamically developing branch of computer vision due to the numerous practical applications and high complexity of data. There exist a need for introducing novel machine learning methods, that can tackle high dimensionality and large number of classes in these images. In this paper, we introduce a novel ensemble me...
Chapter
Headache, medically known as cephalalgia, may have a wide range of symptoms and its types may be related and mixed. Its proper diagnosis is difficult and automatic diagnosis is usually rather imprecise, therefore, the problem is still the focus of intensive research. In the paper we propose headache diagnosis method which makes the decision on the...
Chapter
Hyperspectral image analysis is among one of the current trends in computer vision and machine learning. Due to the high dimensionality, large number of classes, presence of noise and complex structure, this is not a trivial task. There exists a need for more precise and computationally efficient algorithms for hyperspectral image segmentation and...

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Projects

Projects (4)
Project
- traffic modeling - traffic prediction - traffic-aware network optimization and survivability provisioning
Project
SocialTruth provides an innovative and distributed way to achieve both content and author credibility verification and detection of fake news increasing, thus, the trust in Social Media. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825477.
Project
The main objective of the project is to develop, implement, and analyze models and algorithms for optimization of cognitive optical networks. In this project, we form a hypothesis that it is possible to develop new optimization methods in order to improve performance of optical networks by utilizing additional information provided by cognitive processes including data analytics mechanisms based on machine learning methods. Using the developed optimization models and methods, we plan to thoroughly examine in what extent and in which scenarios the concept of cognitive optical networks can improve the network performance comparing to conventional mechanisms currently used in optical networks.