NDLI: A Text Classification Method with an Effective Feature Extraction Based on Category Analysis

Content Provider	IEEE Xplore Digital Library
Author	Yun Li Yan Sheng Luan Luan Ling Chen
Copyright Year	2009
Abstract	Text classification refers to determine the class of an unknown text according to its content in the given classification system. In order to extract fewer features to express the information in the text as much as possible, the paper analysis the various features’ statistical properties and to extract the global features according to Zipf's law; and then, based on the statistical analysis of the features’ classified information, the efficient feature is extracted by computing the contribute of a feature; After that, the traditional TF-IDF formula is improved using category frequencies named by TF-IDF-CF for calculating the feature weight; Finally the text classification method is proposed. The experiment results illustrate that feature extraction methods proposed in the paper are effective and the formula TF-IDF-CF for calculating the feature weight has higher classification accuracy.
Starting Page	95
Ending Page	99
File Size	563315
Page Count	5
File Format	PDF
ISBN	9780769537351
DOI	10.1109/FSKD.2009.304
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Knowledge engineering Costs Statistical analysis Zipf Law Feature Weight Feature Extraction Information management Data mining Information analysis Category Frequency Text categorization Feature extraction Frequency Fuzzy systems
Content Type	Text
Resource Type	Article

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