.png){kind=icon}
.png){kind=icon}
Return
Feature subset selection using naive Bayes for text classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2015.07.028.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Bayesian model averaging
Global selection index
Latent selection augmented naive Bayes
Local selection index
Text classification
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.3
Papers:
7.8K
Citations:
1.6W