关键词: 数据流, 非平衡数据, 概念漂移, Kappa系数, 分类算法

Abstract:

Online class imbalance learning is an important research problem in the field of online learning and class imbalanced learning. It is used to process data streams with much skewed class distribution. Such problems are common in practical scenarios, such as fault diagnosis of real-time control monitoring systems and intrusion detection in computer networks. Due to the concept drift phenomenon and imbalance problem in the dynamic data streams, the algorithm not only deals with concept drift, but also solves class imbalance problems. In view of the above problems, a method for processing imbalanced data streams while detecting concept drift is proposed. This algorithm uses Kappa coefficient to detect the concept drift, and then detects the balance rate, and finally updates the classifier. Experimental results show that the algorithm has better classification performance for imbalanced data streams on different evaluation indexes.

Key words: data streams, imbalanced data, concept drift, Kappa coefficient, classification algorithm