Intensive Class Imbalance Learning in Drifting Data Streams

Abstract

Streaming data analysis faces two primary challenges: concept drifts and class imbalance. The co-occurrence of virtual drifts and class imbalance is a common real-world scenario requiring dedicated solutions. This paper presents Intensive Class Imbalance Learning (ICIL), a novel supervised classification method for virtually drifting data streams. ICIL facilitates the detection of virtual drifts through a feature-sensitive change detection method. It calibrates the data over time to resolve within-class imbalance, overlaps, and small sample size problems. A weighted voting ensemble is proposed for enhanced performance, wherein weights are constantly updated based on the recent performance of the member classifiers. Experiments are conducted on 14 synthetic and real-world data streams to demonstrate the efficacy of the proposed method. The comparative analysis against 11 state-of-the-art methods shows that the proposed method outperforms the other methods in 9/14 data streams on the G-mean metric.