Feature subset selection using naive Bayes for text classification

作     者：Guozhong Feng Jianhua Guo Bing-Yi Jing Tieli Sun 

作者机构：Key Laboratory for Applied Statistics of MOE School of Mathematics and Statistics Northeast Normal University Changchun 130024 China Key Laboratory of Intelligent Information Processing of Jilin Universities School of Computer Science and Information Technology Northeast Normal University Changchun 130117 China Department of Mathematics Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong 

出 版 物：《Pattern Recognition Letters》 (模式识别快报)

年 卷 期：2015年第65卷第Nov.1期

页      面：109-115页

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Bayesian model averaging Global selection index Latent selection augmented naive Bayes Local selection index Text classification 

摘      要：Feature subset selection is known to improve text classification performance of various classifiers . The model using the selected features is often regarded as if it had generated the data. By taking its uncertainty into account, the discrimination capabilities can be measured by a global selection index (GSI), which can be used in the prediction function. In this paper, we propose a latent selection augmented naive (LSAN) Bayes classifier. By introducing a latent feature selection indicator, the GSI can be factorized into each local selection index (LSI). Using conjugate priors , the LSI for feature evaluation can be explicitly calculated. Then the feature subset selection models can be pruned by thresholding the LSIs, and the LSAN classifier can be achieved by the product of a small percentage of single feature model averages. The numerical results on some real datasets show that the proposed method outperforms the contrast feature weighting methods, and is very competitive if compared with some other commonly used classifiers such as SVM.