Parameter Distribution Ensemble Learning for Sudden Concept Drift Detection

• Published in: Intelligent Information and Database Systems pp. 192 - 203
• Main Authors: Nguyen, Khanh-Tung, Tran, Trung, Nguyen, Anh-Duc, Phan, Xuan-Hieu, Ha, Quang-Thuy
• Format: Book Chapter
• Language: English
• Published: Cham Springer Nature Switzerland
• Series: Lecture Notes in Computer Science
• Subjects: Bayesian optimization; Concept drift; Data stream; Ensemble learning
• ISBN: 9783031219665; 303121966X
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-031-21967-2_16

Concept drift is a big challenge in data stream mining (including process mining) since it seriously decreases the accuracy of a model in online learning problems. Model adaptation to changes in data distribution before making new predictions is very necessary. This paper proposes a novel ensemble method called E-ERICS, which combines multiple Bayesian-optimized ERICS models into one model and uses a voting mechanism to determine whether each instance of a data stream is a concept drift point or not. The experimental results on the synthetic and classic real-world streaming datasets showed that the proposed method is much more precise and more sensitive (shown in F1-score, precision, and recall metrics) than the original ERICS models in detecting concept drift, especially a sudden drift.