NDLI: An improved method for feature weighting to document categorization

Content Provider	IEEE Xplore Digital Library
Author	Zhili Pei Yuxin Zhou Lihua Wang Lisha Liu Yinan Lu Ying Kong
Copyright Year	2010
Description	Author affiliation: College of Computer Science and Technology, Inner Mongolia University for the Nationalites, Tongliao, China (Zhili Pei; Yuxin Zhou) \|\| Automation Workstation, People's Laborational Army 65367 Troops, Tonghua, China (Lisha Liu) \|\| College of Mechanical Engineering, Inner Mongolia University for the Nationalities, Tongliao, China (Lihua Wang) \|\| College of Computer Science and Technology, Jilin University, Changchun, China (Yinan Lu) \|\| College of Basic Medical, Dalian Medicaldd University, Dalian, China (Ying Kong)
Abstract	Based on the significance of feature frequency, an improved weighting method for feature frequency is proposed, which considers the decision-making information in evaluating the contribution of feature frequency to document categorization. Experimental results show that this method could improve the space distribution of the document set. Furthermore, the values of macro accuracy, macro recall rate and macroF1 are all improved significantly.
File Size	193103
File Format	PDF
ISBN	9781424458219
e-ISBN	9781424458240
DOI	10.1109/ICFCC.2010.5497607
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Automation Decision making Educational institutions tf.idf real rough set document categorization feature weighting Mechanical engineering Computer science Fuzzy sets Machine learning Frequency Set theory Workstations
Content Type	Text
Resource Type	Article

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