主  題： Feature subset selection using naive Bayes for text classification

內容簡介：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.

報告人：郭建華    教授    博導    副校長

時  間：2017-04-22    10:00

地  點：競慧東樓302

舉辦單位：理學院  統計科學與大數據研究院  科研部