Machine learning in concept drift detection using statistical measures

作     者：Ali Abdu, Nail Adeeb Basulaim, Khaled Omer 

作者机构：Information Technology Department Faculty of Engineering & Computing University of Science and Technology Aden Yemen Information Technology Engineering Department Faculty of Engineering University of Aden Aden Yemen 

出 版 物：《International Journal of Computers and Applications》 (Int J Comput Appl)

年 卷 期：2024年第46卷第5期

页      面：281-291页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Concept drift data streams detection rate for concept drift Kolmogorov–Smirnov statistical test (K–S) statistical t-test Wilcoxon signed rank statistical test 

摘      要：In the data stream, the data has non-stationary quality because of continual and inconsistent change. This change is represented as the concept drift in the classifying process of the streaming data. Representing this data drift concept in data stream mining requires pre-labeled samples. However, labeling samples in real-time streaming (online) is not feasible due to resource utilization and time constraints. Therefore, this paper proposes the concept of Probabilistic Concept Drift Detection (PCDD) in the group classifier. PCDD relies on the data stream classification process and provides concept drift without labeled samples. The PCDD model is evaluated through an empirical study on a dataset called Poker-Hand. The study results show a high concept drift detection rate and a significant reduction in false alarms and missed detections compared to contemporary models. Hence, the results of the experimental study prove the accuracy and scalability of the PCDD model. © 2023 Informa UK Limited, trading as Taylor & Francis Group.