Weighted Ensemble with one-class Classification and Over-sampling and Instance selection (WECOI): An approach for learning from imbalanced data streams

Abstract

Learning from imbalanced data streams is one of the challenges associated with classification algorithms and learning classifiers. The goal of this paper is to propose and validate a new approach for learning from data streams, with reference to the problem of class-imbalanced data. A hybrid approach for changing the class distribution towards a more balanced dataset using over-sampling and instance selection techniques is discussed. The proposed approach is based on the integration of a weighted ensemble classification and a technique to deal with the problem of class imbalance, and is called Weighted Ensemble with one-class Classification and Over-sampling and Instance selection (WECOI). Our approach assumes that classifiers are induced from incoming blocks of instances, called data chunks. These data chunks consist of incoming instances from different classes, and a balance between them is reached through our hybrid approach. These data chunks are then used to induce classifier ensembles. The proposed approach is validated experimentally using several selected benchmark datasets, and the results of computational experiments are presented and discussed. The results show that the proposed approach for eliminating class imbalance in data streams can help increase the performance of online learning algorithms. This is an extended version of a paper that was presented at the International Conference on Computational Science in 2021 (ICCS-2021) (Czarnowski, 2021). This version has been enriched with a deeper analysis of the results obtained from a modified variant of the originally proposed approach.