JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2016, Vol. 51 ›› Issue (5): 87-93.doi: 10.6040/j.issn.1671-9352.1.2015.E17

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Feature selection combined with the global and local information(GLFS)

WAN Zhong-ying, WANG Ming-wen, ZUO Jia-li, WAN Jian-yi   

  1. School of Computer Information Engineering, Jiangxi Normal University, Nanchang 330022, Jiangxi, China
  • Received:2015-09-25 Online:2016-05-20 Published:2016-05-16

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Abstract: Feature selection methods directly affect the effect of text categorization. Traditional feature selection algorithm is based on global approach, ignoring the influence of local features, and even makes a lot of training document has no features. Therefore, the paper proposed a feature selection algorithm combined with the ALOFT method, which unify the traditional globe features and contribution rate of a word to individual document to unify the global and local information(GLFS). Experimental results in the Reuters data set and Fudan data set show that the method can ensure that each document has a characteristic word and improve classification performance. Furthermore, the paper discussed the influence of the new method of feature weights to classification.

Key words: the global and local information, feature selection, text classification, ALOFT, feature weight

CLC Number: 

  • TP391
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