A Novel Feature Voting Model for Text Classification

摘要：

　　Along with the information explosion in the Internet era,the traditional classification methods,such as KNN(knearest neighbor),Na(i)ve Bayes(NB),encounter bottlenecks due to the endless stream of new words.In this paper,through comparing with the Rocchio and Bayesian algorithms,it has been found that centroid-based algorithms are insufficient for text classification.Therefore,a novel feature voting model is proposed,which gives rise to a bag-of-words based feature voting algorithm for text classification.This algorithm assigns categories for each document according to the ranking of weighted sum of feature values.Experimental results have shown the efficiency of the proposed method over the other state-of-the-art methods.

关键词： Naive Bayes text classification feature voting

作者: Sen Jia Jinquan Liang Yao Xie Lin Deng

作者单位: Key Laboratory of Spatial Information Intelligent Perception and Services,Shenzhen University,Guangdong,China

会议类型: 国际会议

会议名称: The 2014 10th International Conference on Natural Computation (ICNC 2014) and the 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2014)(第十届自然计算和第十一届模糊系统与知识发现国际会议)

会议地点: 厦门

会议语种:英文

页码: 314-319

在线出版日期: 2014-08-19（万方平台首次上网日期，不代表论文的发表时间）