关键词: 概念漂移, 倾斜数据流, 重采样, 分类, 正类

Abstract: The concept drift is common in skewed data stream (SDS). However, the most detection algorithms of concept drift assume that the class distributions of data streams are balanced, and are not suitable in skewed data streams. Therefore, this paper proposes a detection approach for concept drifts in SDS, called CDPSD. Firstly, it adopts the modified resample method, which makes the instances in different concepts belong to different data blocks, and then builds the classifiers. Secondly, it uses the class distribution of the positive not all instances to detect the concept drifts and modify the classifiers. The experiments show that CDPSD can detect the concept drift, update the classifier in time, and promote the classification results of positive instances.

Key words: concept drift, skewed data streams, resample, classification, positive class