Impact of Clustering on a Synthetic Instance Generation in Imbalanced Data Streams Classification
Czarnowski I, Martins DML
Abstract
The goal of the paper is to propose a new version of the Weighted Ensemble with one-class Classification and Over-sampling and Instance selection (WECOI) algorithm. This paper describes WECOI and presents the alternative approach for over-sampling, which is based on a selection of reference instances from produced clusters. This approach is flexible on applied clustering methods; however, the similarity-based clustering algorithm has been proposed as a core. For clustering, different methods may also be applied. The proposed approach has been validated experimentally using different clustering methods and shows how the clustering technique may influence synthetic instance generation and the performance of WECOI. The WECOI approach has also been compared with other algorithms dedicated to learning from imbalanced data streams. The computational experiment was carried out using several selected benchmark datasets. The computational experiment results are presented and discussed.
Keywords
Classification; Learning from data streams; Imbalanced data; Over-sampling; Clustering