Improved Relative Term Frequency Probability Feature Selection for Document Categorization

Abstract:

Feature selection is an important process to choose a subset of features relevant to a particular application in document classification. Those terms which occur unevenly in various categories have strong distinguishable information as to categorization. Firstly, based on the categorical document frequency probability (CTFP), a CTFP_VM feature selection algorithm was designed for feature selection. Secondly, a maximum term frequency conditional distribution factor was proposed to improve the CTFP_VM criterion further. We perform the document categorization experiments on SVM classifiers with the well-known Reuters-21578 and 20news-18828 corpuses as unbalanced and balanced corpus respectively. Experiments compare the novel methods with other conventional feature selection algorithms and the proposed method achieves the excellent feature set for document categorization.

• Periodical: Applied Mechanics and Materials (Volumes 548-549)
• Pages: 1102-1109
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.548-549.1102
• Online since: April 2014
• Authors: Qiang Li, Liang He, Xin Lin
• Keywords: Categorical Distribution, Category Tendency, Distribution Probability, Term Frequency, Variance Mean

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