Incremental learning method of Bayesian classification combined with feedback information
XU Ming-ying1,2,WEI Yong-qing3,ZHAO Jing1,2
1. School of Information Science and Engineering, Shandong Normal University, Jinan Shandong 250014, China 2. Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Jinan Shandong 250014, China 3. Basic Education Department, Shandong Police College, Jinan Shandong 250014, China
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.
2011, Vol. 31 
