Jerzy Stefanowski

Maciej Zieba

Proceedings of the ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain, 2024

2023

Reproducibility of Machine Learning: Terminology, Recommendations and Open Issues.

[BibT_eX]

[DOI]

CoRR, 2023

PIQARD System for Experimenting and Testing Language Models with Prompting Strategies.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track, 2023

Deep Similarity Learning Loss Functions in Data Transformation for Class Imbalance.

[BibT_eX]

[DOI]

Damian Horna

Proceedings of the Fifth International Workshop on Learning with Imbalanced Domains: Theory and Applications, 2023

The Problem of Coherence in Natural Language Explanations of Recommendations.

[BibT_eX]

[DOI]

Jakub Raczynski

Proceedings of the ECAI 2023 - 26th European Conference on Artificial Intelligence, September 30 - October 4, 2023, Kraków, Poland, 2023

Multi-criteria Approaches to Explaining Black Box Machine Learning Models.

[BibT_eX]

[DOI]

Proceedings of the Intelligent Information and Database Systems - 15th Asian Conference, 2023

2022

What makes multi-class imbalanced problems difficult? An experimental study.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2022

The Influence of Multiple Classes on Learning Online Classifiers from Imbalanced and Concept Drifting Data Streams.

[BibT_eX]

[DOI]

Agnieszka Lipska

CoRR, 2022

Quality versus speed in energy demand prediction for district heating systems.

[BibT_eX]

[DOI]

CoRR, 2022

The Influence of Multiple Classes on Learning from Imbalanced Data Streams.

[BibT_eX]

[DOI]

Agnieszka Lipska

Proceedings of the Fourth International Workshop on Learning with Imbalanced Domains: Theory and Applications, 2022

Quality Versus Speed in Energy Demand Prediction - Experience Report from an R &D project.

[BibT_eX]

[DOI]

Proceedings of the Database and Expert Systems Applications, 2022

2021

The impact of data difficulty factors on classification of imbalanced and concept drifting data streams.

[BibT_eX]

[DOI]

Knowl. Inf. Syst., 2021

Classification of Multi-class Imbalanced Data: Data Difficulty Factors and Selected Methods for Improving Classifiers.

[BibT_eX]

[DOI]

Proceedings of the Rough Sets - International Joint Conference, 2021

Prototypical Convolutional Neural Network for a Phrase-Based Explanation of Sentiment Classification.

[BibT_eX]

[DOI]

Kamil Plucinski

Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021

Time Aspect in Making an Actionable Prediction of a Conversation Breakdown.

[BibT_eX]

[DOI]

Piotr Janiszewski

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, 2021

Two Ways of Extending BRACID Rule-based Classifiers for Multi-class Imbalanced Data.

[BibT_eX]

[DOI]

Maria Naklicka

Proceedings of the Third International Workshop on Learning with Imbalanced Domains: Theory and Applications, 2021

2020

On the Dynamics of Classification Measures for Imbalanced and Streaming Data.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2020

multi-imbalance: Open Source Python Toolbox for Multi-class Imbalanced Classification.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track, 2020

2019

Using Information on Class Interrelations to Improve Classification of Multiclass Imbalanced Data: A New Resampling Algorithm.

[BibT_eX]

[DOI]

Malgorzata Janicka

Int. J. Appl. Math. Comput. Sci., 2019

2018

Improving Bagging Ensembles for Class Imbalanced Data by Active Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Feature Selection for Data and Pattern Recognition, 2018

Multi-class and feature selection extensions of Roughly Balanced Bagging for imbalanced data.

[BibT_eX]

[DOI]

J. Intell. Inf. Syst., 2018

Visual-based analysis of classification measures and their properties for class imbalanced problems.

[BibT_eX]

[DOI]

Inf. Sci., 2018

ImWeights: Classifying Imbalanced Data Using Local and Neighborhood Information.

[BibT_eX]

[DOI]

Proceedings of the Second International Workshop on Learning with Imbalanced Domains: Theory and Applications, 2018

Local Data Characteristics in Learning Classifiers from Imbalanced Data.

[BibT_eX]

[DOI]

Proceedings of the Advances in Data Analysis with Computational Intelligence Methods, 2018

2017

Stream Classification.

[BibT_eX]

[DOI]

Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Prequential AUC: properties of the area under the ROC curve for data streams with concept drift.

[BibT_eX]

[DOI]

Knowl. Inf. Syst., 2017

Ensemble learning for data stream analysis: A survey.

[BibT_eX]

[DOI]

Inf. Fusion, 2017

Visual-Based Analysis of Classification Measures with Applications to Imbalanced Data.

[BibT_eX]

[DOI]

CoRR, 2017

Exploring complex and big data.

[BibT_eX]

[DOI]

Krzysztof Krawiec

Robert Wrembel

Int. J. Appl. Math. Comput. Sci., 2017

Tetrahedron: Barycentric Measure Visualizer.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Evaluating Difficulty of Multi-class Imbalanced Data.

[BibT_eX]

[DOI]

Proceedings of the Foundations of Intelligent Systems - 23rd International Symposium, 2017

Actively Balanced Bagging for Imbalanced Data.

[BibT_eX]

[DOI]

Proceedings of the Foundations of Intelligent Systems - 23rd International Symposium, 2017

Discovering Minority Sub-clusters and Local Difficulty Factors from Imbalanced Data.

[BibT_eX]

[DOI]

Proceedings of the Discovery Science - 20th International Conference, 2017

An Algorithm for Selective Preprocessing of Multi-class Imbalanced Data.

[BibT_eX]

[DOI]

Szymon Wojciechowski

Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017, 2017

2016

Dealing with Data Difficulty Factors While Learning from Imbalanced Data.

[BibT_eX]

[DOI]

Proceedings of the Challenges in Computational Statistics and Data Mining, 2016

Types of minority class examples and their influence on learning classifiers from imbalanced data.

[BibT_eX]

[DOI]

J. Intell. Inf. Syst., 2016

Post-processing of BRACID Rules Induced from Imbalanced Data.

[BibT_eX]

[DOI]

Fundam. Informaticae, 2016

PUT at SemEval-2016 Task 4: The ABC of Twitter Sentiment Analysis.

[BibT_eX]

[DOI]

Proceedings of the 10th International Workshop on Semantic Evaluation, 2016

Consistency Driven Feature Subspace Aggregating for Ordinal Classification.

[BibT_eX]

[DOI]

Proceedings of the Rough Sets - International Joint Conference, 2016

Increasing the Interpretability of Rules Induced from Imbalanced Data by Using Bayesian Confirmation Measures.

[BibT_eX]

[DOI]

Izabela Szczech

Proceedings of the New Frontiers in Mining Complex Patterns - 5th International Workshop, 2016

Application of Preprocessing Methods to Imbalanced Clinical Data: An Experimental Study.

[BibT_eX]

[DOI]

Proceedings of the Information Technologies in Medicine - 5th International Conference, 2016

Ensemble Diversity in Evolving Data Streams.

[BibT_eX]

[DOI]

Proceedings of the Discovery Science - 19th International Conference, 2016

2015

SMOTE-IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering.

[BibT_eX]

[DOI]

Inf. Sci., 2015

Data stream classification and big data analytics.

[BibT_eX]

[DOI]

Bartosz Krawczyk

Michal Wozniak

Neurocomputing, 2015

Neighbourhood sampling in bagging for imbalanced data.

[BibT_eX]

[DOI]

Neurocomputing, 2015

Abstaining in rule set bagging for imbalanced data.

[BibT_eX]

[DOI]

Log. J. IGPL, 2015

Addressing imbalanced data with argument based rule learning.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2015

Adaptive Ensembles for Evolving Data Streams - Combining Block-Based and Online Solutions.

[BibT_eX]

[DOI]

Proceedings of the New Frontiers in Mining Complex Patterns - 4th International Workshop, 2015

The Usefulness of Roughly Balanced Bagging for Complex and High-Dimensional Imbalanced Data.

[BibT_eX]

[DOI]

Proceedings of the New Frontiers in Mining Complex Patterns - 4th International Workshop, 2015

On Properties of Undersampling Bagging and Its Extensions for Imbalanced Data.

[BibT_eX]

[DOI]

Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015, 2015

2014

Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2014

Open challenges for data stream mining research.

[BibT_eX]

[DOI]

SIGKDD Explor., 2014

Processing and mining complex data streams.

[BibT_eX]

[DOI]

Alfredo Cuzzocrea

Dominik Slezak

Inf. Sci., 2014

Combining block-based and online methods in learning ensembles from concept drifting data streams.

[BibT_eX]

[DOI]

Inf. Sci., 2014

The Impact of Local Data Characteristics on Learning from Imbalanced Data.

[BibT_eX]

[DOI]

Proceedings of the Rough Sets and Intelligent Systems Paradigms, 2014

Prequential AUC for Classifier Evaluation and Drift Detection in Evolving Data Streams.

[BibT_eX]

[DOI]

Proceedings of the New Frontiers in Mining Complex Patterns - Third International Workshop, 2014

Local Characteristics of Minority Examples in Pre-processing of Imbalanced Data.

[BibT_eX]

[DOI]

Malgorzata Trzcielinska

Proceedings of the Foundations of Intelligent Systems - 21st International Symposium, 2014

RILL: Algorithm for Learning Rules from Streaming Data with Concept Drift.

[BibT_eX]

[DOI]

Magdalena Deckert

Proceedings of the Foundations of Intelligent Systems - 21st International Symposium, 2014

Managing Borderline and Noisy Examples in Imbalanced Classification by Combining SMOTE with Ensemble Filtering.

[BibT_eX]

[DOI]

Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2014, 2014

2013

Extending Bagging for Imbalanced Data.

[BibT_eX]

[DOI]

Lukasz Idkowiak

Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013, 2013

2012

BRACID: a comprehensive approach to learning rules from imbalanced data.

[BibT_eX]

[DOI]

J. Intell. Inf. Syst., 2012

IIvotes ensemble for imbalanced data.

[BibT_eX]

[DOI]

Intell. Data Anal., 2012

Modifications of Classification Strategies in Rule Set Based Bagging for Imbalanced Data.

[BibT_eX]

[DOI]

Proceedings of the Hybrid Artificial Intelligent Systems - 7th International Conference, 2012

Identification of Different Types of Minority Class Examples in Imbalanced Data.

[BibT_eX]

[DOI]

Proceedings of the Hybrid Artificial Intelligent Systems - 7th International Conference, 2012

Comparing Block Ensembles for Data Streams with Concept Drift.

[BibT_eX]

[DOI]

Magdalena Deckert

Proceedings of the New Trends in Databases and Information Systems, 2012

2011

Accuracy Updated Ensemble for Data Streams with Concept Drift.

[BibT_eX]

[DOI]

Proceedings of the Hybrid Artificial Intelligent Systems - 6th International Conference, 2011

Local neighbourhood extension of SMOTE for mining imbalanced data.

[BibT_eX]

[DOI]

Tomasz Maciejewski

Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, 2011

2010

Variable Consistency Bagging Ensembles.

[BibT_eX]

[DOI]

Trans. Rough Sets, 2010

Learning from Imbalanced Data in Presence of Noisy and Borderline Examples.

[BibT_eX]

[DOI]

Proceedings of the Rough Sets and Current Trends in Computing, 2010

Argument Based Generalization of MODLEM Rule Induction Algorithm.

[BibT_eX]

[DOI]

Proceedings of the Rough Sets and Current Trends in Computing, 2010

Ordinal Classification with Monotonicity Constraints by Variable Consistency Bagging.

[BibT_eX]

[DOI]

Proceedings of the Rough Sets and Current Trends in Computing, 2010

Integrating Selective Pre-processing of Imbalanced Data with Ivotes Ensemble.

[BibT_eX]

[DOI]

Proceedings of the Rough Sets and Current Trends in Computing, 2010

2009

An experimental evaluation of two approaches to mining context based sequential patterns.

[BibT_eX]

[DOI]

Radoslaw Z. Ziembinski

Control. Cybern., 2009

Ensembles of Abstaining Classifiers Based on Rule Sets.

[BibT_eX]

[DOI]

Magdalena Zajac

Proceedings of the Foundations of Intelligent Systems, 18th International Symposium, 2009

2008

Selective Pre-processing of Imbalanced Data for Improving Classification Performance.

[BibT_eX]

[DOI]

Proceedings of the Data Warehousing and Knowledge Discovery, 10th International Conference, 2008

2007

On Combined Classifiers, Rule Induction and Rough Sets.

[BibT_eX]

[DOI]

Trans. Rough Sets, 2007

Evaluating Importance of Conditions in the Set of Discovered Rules.

[BibT_eX]

[DOI]

Salvatore Greco

Proceedings of the Rough Sets, 2007

Combining Answers of Sub-classifiers in the Bagging-Feature Ensembles.

[BibT_eX]

[DOI]

Proceedings of the Rough Sets and Intelligent Systems Paradigms, International Conference, 2007

Comprehensible and Accurate Cluster Labels in Text Clustering.

[BibT_eX]

[DOI]

Proceedings of the Computer-Assisted Information Retrieval (Recherche d'Information et ses Applications) - RIAO 2007, 8th International Conference, Carnegie Mellon University, Pittsburgh, PA, USA, May 30, 2007

2006

Rough Sets for Handling Imbalanced Data: Combining Filtering and Rule-based Classifiers.

[BibT_eX]

[DOI]

Fundam. Informaticae, 2006

An Empirical Study of Using Rule Induction and Rough Sets to Software Cost Estimation.

[BibT_eX]

[DOI]

Fundam. Informaticae, 2006

Preface.

[BibT_eX]

[DOI]

Fundam. Informaticae, 2006

Classification of Polish Email Messages: Experiments with Various Data Representations.

[BibT_eX]

[DOI]

Marcin Zienkowicz

Proceedings of the Foundations of Intelligent Systems, 16th International Symposium, 2006

2005

A Comparison of Two Approaches to Data Mining from Imbalanced Data.

[BibT_eX]

[DOI]

Jerzy W. Grzymala-Busse

J. Intell. Manuf., 2005

On Using Rule Induction in Multiple Classifiers with a Combiner Aggregation Strategy.

[BibT_eX]

[DOI]

Slawomir Nowaczyk

Proceedings of the Fifth International Conference on Intelligent Systems Design and Applications (ISDA 2005), 2005

Mining Context Based Sequential Patterns.

[BibT_eX]

[DOI]

Radoslaw Z. Ziembinski

Proceedings of the Advances in Web Intelligence Third International Atlantic Web IntelligenceConference, 2005

2004

Rough Set Theory and Decision Rules in Data Analysis of Breast Cancer Patients.

[BibT_eX]

[DOI]

Trans. Rough Sets, 2004

Incremental versus Non-incremental Rule Induction for Multicriteria Classification.

[BibT_eX]

[DOI]

Trans. Rough Sets, 2004

Hyperplane Aggregation of Dominance Decision Rules.

[BibT_eX]

[DOI]

Roman Pindur

Robert Susmaga

Fundam. Informaticae, 2004

An experimental evaluation of improving rule based classifiers with two approaches that change representations of learning examples.

[BibT_eX]

[DOI]

Eng. Appl. Artif. Intell., 2004

The Bagging and n2-Classifiers Based on Rules Induced by MODLEM.

[BibT_eX]

[DOI]

Proceedings of the Rough Sets and Current Trends in Computing, 2004

Lingo: Search Results Clustering Algorithm Based on Singular Value Decomposition.

[BibT_eX]

Stanislaw Osinski

Proceedings of the Intelligent Information Processing and Web Mining, 2004

2003

Web Search Results Clustering in Polish: Experimental Evaluation of Carrot.

[BibT_eX]

Proceedings of the Intelligent Information Processing and Web Mining, 2003

Incremental Rule Induction for Multicriteria and Multiattribute Classification.

[BibT_eX]

Marcin Zurawski

Proceedings of the Intelligent Information Processing and Web Mining, 2003

Carrot and Language Properties in Web Search Results Clustering.

[BibT_eX]

[DOI]

Proceedings of the Web Intelligence, 2003

2002

Application of Rule Induction and Rough Sets to Verification of Magnetic Resonance Diagnosis.

[BibT_eX]

[DOI]

Krzysztof Slowinski

Dariusz Siwinski

Fundam. Informaticae, 2002

Induction of Decision Rules and Classification in the Valued Tolerance Approach.

[BibT_eX]

[DOI]

Proceedings of the Rough Sets and Current Trends in Computing, 2002

Importance and Interaction of Conditions in Decision Rules.

[BibT_eX]

[DOI]

Proceedings of the Rough Sets and Current Trends in Computing, 2002

Mining Association Rules in Preference-Ordered Data.

[BibT_eX]

[DOI]

Salvatore Greco

Proceedings of the Foundations of Intelligent Systems, 13th International Symposium, 2002

Bagging and Induction of Decision Rules.

[BibT_eX]

Proceedings of the Intelligent Information Systems 2002, 2002

2001

Evaluating business credit risk by means of approach-integrating decision rules and case-based learning.

[BibT_eX]

[DOI]

Intell. Syst. Account. Finance Manag., 2001

Induction of decision rules in classification and discovery-oriented perspectives.

[BibT_eX]

[DOI]

Daniel Vanderpooten

Int. J. Intell. Syst., 2001

Three discretization methods for rule induction.

[BibT_eX]

[DOI]

Jerzy W. Grzymala-Busse

Int. J. Intell. Syst., 2001

Incomplete Information Tables and Rough Classification.

[BibT_eX]

[DOI]

Comput. Intell., 2001

2000

Valued Tolerance and Decision Rules.

[BibT_eX]

[DOI]

Proceedings of the Rough Sets and Current Trends in Computing, 2000

An Algorithm for Induction of Decision Rules Consistent with the Dominance Principle.

[BibT_eX]

[DOI]

Proceedings of the Rough Sets and Current Trends in Computing, 2000

Variable Consistency Model of Dominance-Based Rough Sets Approach.

[BibT_eX]

[DOI]

Proceedings of the Rough Sets and Current Trends in Computing, 2000

1999

On the Extension of Rough Sets under Incomplete Information.

[BibT_eX]

[DOI]

Proceedings of the New Directions in Rough Sets, 1999

1998

Handling Continuous Attributes in Discovery of Strong Decision Rules.

[BibT_eX]

[DOI]

Proceedings of the Rough Sets and Current Trends in Computing, 1998

ROSE - Software Implementation of the Rough Set Theory.

[BibT_eX]

[DOI]

Proceedings of the Rough Sets and Current Trends in Computing, 1998

Experiments on Solving Multiclass Learning Problems by n2-classifier.

[BibT_eX]

[DOI]

Jacek Jelonek

Proceedings of the Machine Learning: ECML-98, 1998

1997

Feature subset selection for classification of histological images.

[BibT_eX]

[DOI]

Jacek Jelonek

Artif. Intell. Medicine, 1997

Rough Set Theory and Rule Induction Techniques for Discovery of Attribute Dependencies in Medical Information Systems.

[BibT_eX]

[DOI]

Krzysztof Slowinski

Proceedings of the Principles of Data Mining and Knowledge Discovery, 1997

1996

Rough Sets: Facts Versus Misconceptions.

[BibT_eX]

Jerzy W. Grzymala-Busse

Wojciech Ziarko

Informatica (Slovenia), 1996

Rough-Set Reasoning about Uncertain Data.

[BibT_eX]

[DOI]

Fundam. Informaticae, 1996

1993

A General Two-Stage Approach to Inducing Rules from Examples.

[BibT_eX]

Daniel Vanderpooten

Proceedings of the Rough Sets, 1993

Handling Various Types of Uncertainty in the Rough Set Approach.

[BibT_eX]

Proceedings of the Rough Sets, 1993

Neural Networks and Rough Sets - Comparison and Combination for Classification of Histological Pictures.

[BibT_eX]

Proceedings of the Rough Sets, 1993

1992

The Rough Sets Approach to Knowledge Analysis for Classification Support in Technical Diagnostics of Mechanical Objects.

[BibT_eX]

[DOI]

Ryszard Nowicki

Proceedings of the Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, 1992

'Roughdas' and 'Roughclass' Software Implementations of the Rough Sets Approach.

[BibT_eX]

[DOI]

Proceedings of the Intelligent Decision Support, 1992

Analysis of Diagnostic Symptoms in Vibroacoustic Diagnostics by Means of the Rough Sets Theory.

[BibT_eX]

[DOI]

Ryszard Nowicki