Data-driven global-ranking local feature selection methods for text categorization. Issue 4 (March 2015)

• Record Type: Journal Article
• Authors: Pinheiro, Roberto H.W.; Cavalcanti, George D.C.; Ren, Tsang Ing
• Is Part Of: Expert systems with applications. Volume 42:Issue 4(2015)
• Page Start: 1941
• Page End: 1949
• Publication Date: 2015-03
• Subjects: Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2014.10.011
• Languages: English
• ISSNs: 0957-4174

Abstract

<abstract xml:lang="en" abstract-type="author-highlights" id="ab005"> <title id="st075">Highlights</title> <sec> <p id="sp005"> <list id="l0005"> <list-item id="u0005"> <label></label> <p id="p0270">We propose two filtering methods for text categorization.</p> </list-item> <list-item id="u0010"> <label></label> <p id="p0275">The proposed methods find the number of features based on a low range parameter.</p> </list-item> <list-item id="u0015"> <label></label> <p id="p0280">The proposal ensures contribution of each document in the final subset of features.</p> </list-item> <list-item id="u0020"> <label></label> <p id="p0285">The proposed methods obtain better results than VR and ALOFT algorithms.</p> </list-item> </list> </p> </sec> </abstract>