A survey of active and passive concept drift handling methods. (10th April 2022)

• Record Type: Journal Article
• Authors: Han, Meng; Chen, Zhiqiang; Li, Muhang; Wu, Hongxin; Zhang, Xilong
• Is Part Of: Computational intelligence. Volume 38:Number 4(2022)
• Page Start: 1492
• Page End: 1535
• Publication Date: 2022-04-10
• Subjects: active methods -- classification -- concept drift -- data stream -- passive methods; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12520
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: At present, concept drift in the nonstationary data stream is showing trends with different speeds and different degrees of severity, which has brought great challenges to many fields like data mining and machine learning. In the past two decades, a lot of methods dedicated to handling concept drift in the nonstationary data stream have emerged. A novel perspective is proposed to classify these methods, and the current concept drift handling methods are comprehensively explained from the active handling methods and the passive handling methods. In particular, active handling methods are analyzed from the perspective of handling one specific type of concept drift and handling multiple types of concept drift, and passive handling methods are analyzed from the perspective of single learner and ensemble learning. Many concept drift handling methods in this survey are analyzed and summarized in terms of the comparing algorithms, learning model, applicable drift type, advantages, and disadvantages of the algorithms. Finally, further research directions are given, including the active and passive mixing methods, class imbalance, the existence of novel class in the data stream, and the noise in the data stream.