Search Results - "Stefanowski, Jerzy"

Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm by Brzezinski, Dariusz, Stefanowski, Jerzy

“…Data stream mining has been receiving increased attention due to its presence in a wide range of applications, such as sensor networks, banking, and…”
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The impact of data difficulty factors on classification of imbalanced and concept drifting data streams by Brzezinski, Dariusz, Minku, Leandro L., Pewinski, Tomasz, Stefanowski, Jerzy, Szumaczuk, Artur

“…Class imbalance introduces additional challenges when learning classifiers from concept drifting data streams. Most existing work focuses on designing new…”
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A Multi–Criteria Approach for Selecting an Explanation from the Set of Counterfactuals Produced by an Ensemble of Explainers by Stepka, Ignacy, Lango, Mateusz, Stefanowski, Jerzy

“…Counterfactuals are widely used to explain ML model predictions by providing alternative scenarios for obtaining more desired predictions. They can be…”
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Using Information on Class Interrelations to Improve Classification of Multiclass Imbalanced Data: A New Resampling Algorithm by Janicka, Małgorzata, Lango, Mateusz, Stefanowski, Jerzy

“…The relations between multiple imbalanced classes can be handled with a specialized approach which evaluates types of examples’ difficulty based on an analysis…”
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Exploring complex and big data by Stefanowski, Jerzy, Krawiec, Krzysztof, Wrembel, Robert

“…This paper shows how big data analysis opens a range of research and technological problems and calls for new approaches. We start with defining the essential…”
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In memoriam Professor Solomon Marcus by Stefanowski, Jerzy

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Artificial Intelligence Research Community and Associations in Poland by Nalepa, Grzegorz J., Stefanowski, Jerzy

“…In last years Artificial Intelligence presented a tremendous progress by offering a variety of novel methods, tools and their spectacular applications. Besides…”
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Incomplete Information Tables and Rough Classification by Stefanowski, Jerzy, Tsoukiàs, Alexis

“…The rough set theory, based on the original definition of the indiscernibility relation, is not useful for analysing incomplete information tables where some…”
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Types of minority class examples and their influence on learning classifiers from imbalanced data by Napierala, Krystyna, Stefanowski, Jerzy

“…Many real-world applications reveal difficulties in learning classifiers from imbalanced data. Although several methods for improving classifiers have been…”
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What makes multi-class imbalanced problems difficult? An experimental study by Lango, Mateusz, Stefanowski, Jerzy

“…Multi-class imbalanced classification is more difficult and less frequently studied than its binary counterpart. Moreover, research on the causes of the…”
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Combining block-based and online methods in learning ensembles from concept drifting data streams by Brzezinski, Dariusz, Stefanowski, Jerzy

“…Most stream classifiers are designed to process data incrementally, run in resource-aware environments, and react to concept drifts, i.e., unforeseen changes…”
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Prequential AUC: properties of the area under the ROC curve for data streams with concept drift by Brzezinski, Dariusz, Stefanowski, Jerzy

“…Modern data-driven systems often require classifiers capable of dealing with streaming imbalanced data and concept changes. The assessment of learning…”
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Multi-class and feature selection extensions of Roughly Balanced Bagging for imbalanced data by Lango, Mateusz, Stefanowski, Jerzy

“…Roughly Balanced Bagging is one of the most efficient ensembles specialized for class imbalanced data. In this paper, we study its basic properties that may…”
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Neighbourhood sampling in bagging for imbalanced data by Błaszczyński, Jerzy, Stefanowski, Jerzy

“…Various approaches to extend bagging ensembles for class imbalanced data are considered. First, we review known extensions and compare them in a comprehensive…”
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A Comparison of Two Approaches to Data Mining from Imbalanced Data by Grzymala-Busse, Jerzy W., Stefanowski, Jerzy, Wilk, Szymon

“…Our objective is a comparison of two data mining approaches to dealing with imbalanced data sets. The first approach is based on saving the original rule set,…”
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SMOTE–IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering by Sáez, José A., Luengo, Julián, Stefanowski, Jerzy, Herrera, Francisco

“…•Noisy and borderline examples in imbalanced datasets harm classifier performance.•Our proposal reduces the noise and makes the class boundaries more…”
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Three discretization methods for rule induction by Grzymala-Busse, Jerzy W., Stefanowski, Jerzy

“…We discuss problems associated with induction of decision rules from data with numerical attributes. Real‐life data frequently contain numerical attributes…”
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On the Dynamics of Classification Measures for Imbalanced and Streaming Data by Brzezinski, Dariusz, Stefanowski, Jerzy, Susmaga, Robert, Szczech, Izabela

“…As each imbalanced classification problem comes with its own set of challenges, the measure used to evaluate classifiers must be individually selected. To help…”
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BRACID: a comprehensive approach to learning rules from imbalanced data by Napierala, Krystyna, Stefanowski, Jerzy

“…In this paper we consider induction of rule-based classifiers from imbalanced data, where one class (a minority class) is under-represented in comparison to…”
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Visual-based analysis of classification measures and their properties for class imbalanced problems by Brzezinski, Dariusz, Stefanowski, Jerzy, Susmaga, Robert, Szczȩch, Izabela

“…•New visualisation technique for analysing classification performance measures.•Online tool that implements the proposed visualisation technique.•Ten visual…”
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