NDLI: Combining feature ranking for text classification

Content Provider	IEEE Xplore Digital Library
Author	Makrehchi, M. Kamel, M.S.
Copyright Year	2007
Description	Author affiliation: Univ. of Waterloo, Waterloo (Makrehchi, M.; Kamel, M.S.)
Abstract	Feature ranking is one of the dimensionality reduction methods. Because of its simplicity and low cost, it is widely used in text classification. One problem with feature ranking methods is their non-robust behavior when applied to different data sets. In other words, the feature ranking methods behave differently from one data set to the other. The problem is more complex when we consider that the performance of feature ranking methods is different when being used by different classifiers. In this paper, a new method based on combining feature rankings is proposed to find the best features among a set of feature rankings. Four preferential voting method are employed to combine feature rankings obtained by eight well-known ranking measures. According to the results, combining methods can offer reliable results that are very close to the best solution without the need to use a classifier. The proposed method is applied to the text classification problem and evaluated on three well-known data sets using SVM classifier.
Starting Page	510
Ending Page	515
File Size	150487
Page Count	6
File Format	PDF
ISBN	9781424409907
DOI	10.1109/ICSMC.2007.4414080
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-07
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Text categorization Support vector machines Support vector machine classification Voting Training data Particle measurements Niobium compounds Pattern analysis Machine intelligence Costs
Content Type	Text
Resource Type	Article

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