• Publications
  • Influence
Learning from imbalanced data: open challenges and future directions
  • B. Krawczyk
  • Computer Science
  • Progress in Artificial Intelligence
  • 22 April 2016
TLDR
This paper aims at discussing open issues and challenges that need to be addressed to further develop the field of imbalanced learning, while at the same time facing new emerging challenges. Expand
  • 611
  • 27
Ensemble learning for data stream analysis: A survey
TLDR
A comprehensive survey of ensemble approaches for data stream analysis.Taxonomy of ensemble algorithms for various data stream mining tasks.Discussion of open research problems. Expand
  • 413
  • 21
  • PDF
Cost-sensitive decision tree ensembles for effective imbalanced classification
TLDR
In this paper, we introduce an effective ensemble of cost-sensitive decision trees for imbalanced classification. Expand
  • 183
  • 6
  • PDF
A survey on data preprocessing for data stream mining: Current status and future directions
TLDR
Data preprocessing and reduction have become essential techniques in current knowledge discovery scenarios, dominated by increasingly large datasets. Expand
  • 183
  • 5
  • PDF
Learning from Imbalanced Data Sets
TLDR
Data Science is a discipline for discovering new and significant relationships, patterns and trends in the examination of large amounts of data. Expand
  • 149
  • 5
Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets
TLDR
We present a study on oversampling for multi-class imbalanced datasets that focuses on the analysis of the class characteristics. Expand
  • 118
  • 5
One-class classifiers with incremental learning and forgetting for data streams with concept drift
TLDR
This paper reports a novel modification of weighted one-class support vector machine, adapted to the non-stationary streaming data analysis with the presence of concept drift. Expand
  • 59
  • 5
  • PDF
Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy
TLDR
We propose a complete, fully automatic and efficient clinical decision support system for breast cancer malignancy grading. Expand
  • 123
  • 4
One-class classifier ensemble pruning and weighting with firefly algorithm
  • B. Krawczyk
  • Mathematics, Computer Science
  • Neurocomputing
  • 20 February 2015
TLDR
This paper introduces a novel technique for forming efficient one-class classifier ensembles. Expand
  • 51
  • 4
Kappa Updated Ensemble for drifting data stream mining
TLDR
We propose a new ensemble method named Kappa Updated Ensemble (KUE). Expand
  • 21
  • 4
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