关键词: 反馈信息, 遗传算法, 特征选择, 朴素贝叶斯, 增量学习

Abstract: Owing to the insufficiency of the training sets, the performance of the initial classifier is not satisfactory and can not track the users' needs dynamically. Concerning the defect, an incremental learning method of Bayesian classifier combined with feedback information was proposed. To reduce the redundancy between features effectively and improve representative ability of feedback feature subset, an improved feature selection method based on Genetic Algorithm (GA) was used to choose the best features from feedback sets to amend classifier. The experimental results show that the algorithm optimizes classification significantly and has good overall stability.

Key words: feedback information, Genetic Algorithm (GA), feature selection, Nave Bayesian, incremental learning