Improving Text Categorization by Multicriteria Feature Selection | Zendy

Son Doan | Zendy; Susumu Horiguchi | Zendy

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Improving Text Categorization by Multicriteria Feature Selection

Author(s) -

Son Doan,

Susumu Horiguchi

Publication year - 2005

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2005.p0570

Subject(s) - computer science , feature selection , categorization , text categorization , artificial intelligence , selection (genetic algorithm) , ranking (information retrieval) , feature (linguistics) , benchmark (surveying) , machine learning , naive bayes classifier , data mining , natural language processing , information retrieval , pattern recognition (psychology) , support vector machine , philosophy , linguistics , geodesy , geography

Text categorization involves assigning a natural language document to one or more predefined classes. One of the most interesting issues is feature selection. We propose an approach using multicriteria ranking of eatures, a new procedure for feature selection, and apply these to text categorization. Experimental results dealing with Reuters-21578 and 20Newsgroups benchmark data and the naive Bayes algorithm show that our proposal outperforms conventional feature selection in text categorization performance.

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