关键词: 数据流分类, 信息增益, 增量特征选择, 概念漂移

Abstract: The complexity of feature selection for real-world data stream will increase because of high-dimensional data and concept drifting. Information gain is one of the most effective feature selections, but its computation is too huge. In order to deal with the problem, the authors proposed an incremental feature selection algorithm based on improved information gain, named IFS. Firstly, the algorithm selected candidate feature set by using independent evaluation function; secondly, feature set was selected with classifer role in candidate feature set. Finally, it selected feature set again while encountering concept drifting. The experiment was operated on moving hyperplane data set and UCI data set. The experimental results show that the proposed approach can adapt to the concept drifting with higher speed and works much better than non-feature selection algorithms.

Key words: data stream classification, information gain, Incremental Feature Selection (IFS), concept drifting