陈志雄,陈健,闵华清. 基于信息增益的中文文本关联分类[J]. 中文信息学报, 2007, 21(3): 61-68.
CHEN Zhi-xiong, , CHEN Jian, MIN Hua-qing. Chinese Text Based on Information Gain by Associative Classification. , 2007, 21(3): 61-68.
Chinese Text Based on Information Gain by Associative Classification
CHEN Zhi-xiong1, 2, CHEN Jian1, MIN Hua-qing1
1. School of Software Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China; 2. Department of Electronic and Information Engineering, Jiaying University, Meizhou, Guangdong 514015, China
Abstract：Associative classification, which uses association rules in training set to predict the class label for new data object, has been recently reported to achieve higher accuracy than traditional classification approaches like C4.5. The exiting works which are based on support-confidence framework only select the frequent literals to construct classification rules, ignoring the contribution of literals’ classificatory effects. In this paper, a novel associative classification algorithm, named ACIG, is proposed to integrate the effect of information gain and FoilGain for selecting the literals of rules from Chinese text, in order to improve the qualities of literals. Our experimental results show that ACIG outperform other associative classification approach (CPAR) on accuracy.
2007, Vol. 21 
