Preprocessed dynamic classifier ensemble selection for highly imbalanced drifted data streams
| Autor: | Michał Woźniak, Robert Sabourin, Paweł Zyblewski |
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| Rok vydání: | 2021 |
| Předmět: |
Data stream
Concept drift Computer science Data stream mining business.industry Estimator 020206 networking & telecommunications Pattern recognition 02 engineering and technology Computer experiment ComputingMethodologies_PATTERNRECOGNITION Hardware and Architecture Signal Processing 0202 electrical engineering electronic engineering information engineering Preprocessor 020201 artificial intelligence & image processing Data pre-processing Artificial intelligence business Classifier (UML) Software Information Systems |
| Zdroj: | Information Fusion. 66:138-154 |
| ISSN: | 1566-2535 |
| DOI: | 10.1016/j.inffus.2020.09.004 |
| Popis: | This work aims to connect two rarely combined research directions, i.e., non-stationary data stream classification and data analysis with skewed class distributions. We propose a novel framework employing stratified bagging for training base classifiers to integrate data preprocessing and dynamic ensemble selection methods for imbalanced data stream classification. The proposed approach has been evaluated based on computer experiments carried out on 135 artificially generated data streams with various imbalance ratios, label noise levels, and types of concept drift as well as on two selected real streams. Four preprocessing techniques and two dynamic selection methods, used on both bagging classifiers and base estimators levels, were considered. Experimentation results showed that, for highly imbalanced data streams, dynamic ensemble selection coupled with data preprocessing could outperform online and chunk-based state-of-art methods. |
| Databáze: | OpenAIRE |
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