Konrad Jackowski

Konrad Jackowski
Wroclaw University of Science and Technology | WUT · Department of Systems and Computer Network

Ph.D.

About

33
Publications
1,929
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
331
Citations
Citations since 2017
3 Research Items
158 Citations
20172018201920202021202220230102030
20172018201920202021202220230102030
20172018201920202021202220230102030
20172018201920202021202220230102030
Introduction
Research interests: • method of classifier fusion • classifier selection algorithms • application of genetic algorithms in compound classifier training processes • exploration and exploitation of local competences of classifiers in multiple classifier systems • decision making under Concept Drift
Additional affiliations
October 2013 - September 2014
VŠB-Technical University of Ostrava
Position
  • PostDoc Position
February 2009 - September 2013
The Witelon University of Applied Sciences in Legnica
Position
  • Lecturer
February 2009 - present
Wroclaw University of Science and Technology
Position
  • Professor (Assistant)

Publications

Publications (33)
Article
The diversity of a voting committee is one of the key characteristics of ensemble systems. It determines the benefits that can be obtained through classifier fusion. There are many measures of diversity that can be used in classical decision-making systems which operate in stationary environments. A plethora of algorithms have also been proposed to...
Conference Paper
Data stream classification is fast growing research area due to increasing number of practical applications in modern technology. SPAM filtering, weather forecast are just two well known examples. Nonetheless, high pace of incoming data makes classical algorithm inefficient as they usually use batch processing methods. What more, the characteristic...
Conference Paper
Training of compound ensemble classifier systems might be computationally complex and hence time consuming task. Not only elementary classifiers are to be trained, but also model of the ensemble has to be updated. Therefore, an efficiency of the training shall be considered as a compound quality which consists of not only a classification accuracy...
Article
This paper address the data mining task of classifying data stream with concept drift. The proposed algorithm, named Concept-adapting Evolutionary Algorithm For Decision Tree (CEVOT) does not require any knowledge of the environment in which it operates (e.g. numbers and rates of drifts). The novelty of the approach is combining tree learner and ev...
Chapter
Control of dental millingprocesses is a task which can significantly reduce production costs due to possible savings in time. Appropriate setup of production parameters can be done in a course of optimisation aiming at minimising selected objective function, e.g. time. Nonetheless, the main obstacle here is lack of explicitly defined objective func...
Conference Paper
Clustering is the task of partitioning data objects into groups, so that the objects within a cluster are similar to one another and dissimilar to the objects in other clusters. The efficiency random algorithm for good k is used to estimate the optimal number of clusters. In this research two important clustering algorithms, namely centroid based k...
Article
Full-text available
In this paper a novel Tensor-Based Image Segmentation Algorithm (TBISA) is presented, which is dedicated for segmentation of colour images. A purpose of TBISA is to distinguish specific objects based on their characteristics, i.e. shape, colour, texture, or a mixture of these features. All of those information are available in colour channel data....
Article
Developing system for regression tasks like predicting prices, temperature is not a trivial task. There are many of issues which must be addressed such as: selecting appropriate model, eliminating irrelevant inputs, removing noise, etc. Most of them can be solved by application of machine learning methods. Although most of them were developed for c...
Article
Full-text available
Prediction of poly(lactic-co-glycolic acid) (PLGA) micro-and nanoparticles' dissolution rates plays a significant role in pharmaceutical and medical industries. The prediction of PLGA dissolution rate is crucial for drug manufacturing. Therefore, a model that predicts the PLGA dissolution rate could be beneficial. PLGA dissolution is influenced by...
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
A valid diagnosis of migraine is a non-trivial decision problem. This is due to the fact that migraine can manifest wide range of varied symptoms. Thus, designing a computer aided diagnosis system for that problem remains still a very interesting topic. In this paper we present an ensemble classifier system designed for headache diagnosis. We assum...
Conference Paper
Decision trees are among the most popular classification algorithms due to their knowledge representation in form of decision rules which are easy for interpretation and analysis. Nonetheless, a majority of decision trees training algorithms base on greedy top-down induction strategy which has the tendency to develop too complex tree structures. Th...
Conference Paper
Full-text available
Predicting the dissolution rate of proteins plays a significant role in pharmaceutical/medical applications. The rate of dissolution of Poly Lactic-co-Glycolic Acid (PLGA) micro-and nanoparticles is influenced by several factors. Considering all factors leads to a dataset with three hundred features, making the prediction difficult and inaccurate....
Conference Paper
Full-text available
A suitable regression model for predicting the dissolution profile of Poly (lactic-co-glycolic acid) (PLGA) micro-and nanoparticles can play a significant role in pharmaceutical/medical applications. The rate of dissolution of proteins is influenced by several factors and taking all such influencing factors into account, we have a dataset in hand w...
Article
Currently, methods of combined classification are the focus of intense research. A properly designed group of combined classifiers exploiting knowledge gathered in a pool of elementary classifiers can successfully outperform a single classifier. There are two essential issues to consider when creating combined classifiers: how to establish the most...
Chapter
The paper presents the application of ensemble approach in the prediction of tension in a power plant generator. The proposed Adaptive Splitting and Selection (AdaSS) ensemble algorithm performs fusion of several elementary predictors and is based on the assumption that the fusion should take into account the competence of the elementary predictors...
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
Recognition of an EEG signal is a very complex but very important problem. In this paper we focus on a simplified classification problem which consists of detection finger movement based on an analysis of seven EEG sensors. The signals gathered by each sensor are subsequently classified by the respective classification algorithm, which is based on...
Chapter
Full-text available
A suitable regression model for predicting the dissolution profile of Poly (lactic-co-glycolic acid) (PLGA) micro- and nanoparticles can play a significant role in pharmaceutical/medical applications. The rate of dissolution of proteins is influenced by several factors and taking all such influencing factors into account, we have a dataset in hand...
Article
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 subseq...
Article
E-Mail spam is one of the major problems plaguing the contemporary Internet, causing an inconvenience to an individual user and financial loss to a company. Spam filtering allows for early detection of unwanted messages and separates them from the incoming e-mail. Nonetheless, designing an effective spam detection system is not a trivial task, due...
Conference Paper
The paper presents a cost-sensitive modification of the Adaptive Splitting and Selection (AdaSS) algorithm, which trains a combined classifier based on a feature space partitioning. In this study the algorithm considers constraints put on the cost of selected features, which are one of the key-problems in the clinical decision support systems. The...
Article
Full-text available
There are many methods of decision making by an ensemble of classifiers. The most popular are methods that have their origin in voting method, where the decision of the common classifier is a combination of individual classifiers’ outputs. This work presents comparative analysis of some classifier fusion methods based on weighted voting of classifi...
Conference Paper
Reoccurring Context is a phenomenon being subject of interest in machine learning theory dealing with Concept Drift. Periodic reappearance of contexts naturally encourage designing classifier systems which utilizes their expertize on contexts collected in the past. The paper presents study on EAERC algorithm that gather its knowledge on appearing c...
Conference Paper
The paper presents novel algorithm of decision making in multiple classifier system (MCS), which response is based on weighted fusion of discriminating functions derived from a pool of elementary classifiers. Radial basis function model are used to establish the weights of the classifiers over a feature space. For best exploitation of knowledge col...
Article
Full-text available
The paper presents a novel machine learning algorithm used for training a compound classifier system that consists of a set of area classifiers. Area classifiers recognize objects derived from the respective competence area. Splitting feature space into areas and selecting area classifiers are two key processes of the algorithm; both take place sim...
Conference Paper
The paper presents a novel machine learning method which allows obtaining compound classifier. Its idea bases on splitting feature space into separate regions and choosing the best classifier from available set of recognizers for each region. Splitting and selection take place simultaneously as a part of an optimization process. Evolutionary algori...
Conference Paper
Multiple Classifier Systems are nowadays one of the most promising directions in pattern recognition. There are many methods of decision making by the ensemble of classifiers. The most popular are methods that have their origin in voting method, where the decision of the common classifier is a combination of individual classifiers’ outputs. This wo...
Conference Paper
The Multiple Classifier Systems are nowadays one of the most promising directions in pattern recognition. There are many methods of decision making by the ensemble of classifiers. The most popular are methods that have their origin in voting method, where the decision of the common classifier is a combination of individual classifiers’ decisions. T...
Article
The paper presents the novel adaptive splitting and selection algorithm (AdaSS) used for learning compound pattern recognition system. Splitting a feature space into its constituents and selection of the best area classifier from the pool of available recognizers for each region are key processes of the proposed model. Both take place simultaneousl...

Network

Cited By

Projects

Project (1)
Project
The project relates to machine learning algorithms for data stream classification. The primary objective in the design of such a systems is to provide the highest efficiency, which can be understood as a kind of tradeoff between accuracy and processing time. In order to achieve the goal a model of the system has to be adapted to the specificities. In the case of streaming data classification we should take into account that: (a) the characteristics of data may change over time, which is called Concept Drift, (b) the computational speed of the system must be high enough to allow efficient processing of large amounts of information in an acceptably short time. In the project we plan to develop number of algorithms which ensuring high resistance of classification system to aforementioned concept drift. It is plan to investigate possibility of application of algorithms using a distributed and parallel programming paradigms in order to ensure high processing speed of streaming data. Concluding, we define the following project objectives: 1. Developing new methods of supervised and unsupervised concept drift detection along with respective classification algorithms dedicated for stream processing; 2. Developing new classifier models along with respective adaptive learning algorithms aiming at permanent adjustment classifier parameters to changing characteristics of the data stream, especially ensemble of classifiers; 3. Developing machine learning algorithms using a distributed and parallel programming paradigms.