NDLI: A Rough-Set Feature Selection Model for Classification and Knowledge Discovery

Content Provider	IEEE Xplore Digital Library
Author	Qamar, U.
Copyright Year	2013
Description	Author affiliation: Comput. Eng. Dept., Nat. Univ. of Sci. & Technol. (NUST), Islamabad, Pakistan (Qamar, U.) Feature selection aims to remove features unnecessary to the target concept. Rough-set theory (RST) eliminates unimportant or irrelevant features, thus generating a smaller (than the original) set of attributes with the same, or close to, classificatory power. This paper analyses the effects of rough sets on classification using 10 datasets, each including a decision attribute. Classification accuracy mapped to the type and number of attributes both in the original and the reduced datasets. This generates a framework for applying rough-sets for classification purposes. Rough-sets are then used for knowledge discovery in classification and the conclusion indicate a very significant result that removal of individual numeric attributes has far more effect on classification accuracy than removal of categorical attributes.
Sponsorship	IEEE Syst., Man, Cybern. Soc.
Starting Page	788
Ending Page	793
File Size	271153
Page Count	6
File Format	PDF
ISBN	9781479906529
DOI	10.1109/SMC.2013.139
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-13
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Categorical and Numerical Data Rough-sets Classification Feature Selection
Content Type	Text
Resource Type	Article

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