DocumentCode :
639755
Title :
Rough set based feature selection: A Review
Author :
Anaraki, Javad Rahimipour ; Eftekhari, Mahdi
Author_Institution :
Dept. of Comput. Eng., Shahid Bahonar Univ. of Kerman, Kerman, Iran
fYear :
2013
fDate :
28-30 May 2013
Firstpage :
301
Lastpage :
306
Abstract :
Rough set is a tool with a mathematical foundation to deal with imprecise and imperfect knowledge. It has been widely applied in machine learning, data mining and knowledge discovery. One of the applications of Rough set theory in machine learning is the so-called feature selection especially for classification problems. This is performed by means of finding a reduct set of attributes. Reduct set is a subset of all features which retains classification accuracy as original attributes. Finding a reduct set in decision systems is NP-hard problem which has attracted many researchers to combine different methods with rough set. This paper is a survey of several methods of feature selection using rough set theory.
Keywords :
learning (artificial intelligence); pattern classification; rough set theory; NP-hard problem; classification accuracy; classification problems; decision systems; machine learning; reduct set; rough set based feature selection; rough set theory; Approximation methods; Computational modeling; Educational institutions; Filtering theory; Frequency measurement; Search problems; Set theory; Feature selection; Machine learning; Rough set;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Knowledge Technology (IKT), 2013 5th Conference on
Conference_Location :
Shiraz
Print_ISBN :
978-1-4673-6489-8
Type :
conf
DOI :
10.1109/IKT.2013.6620083
Filename :
6620083
Link To Document :
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