Title :
An attribute reduction method based on SVM
Author :
Tang, Xiao ; Mo, Zhiwen
Author_Institution :
Coll. of Math. & Software Sci., Sichuan Normal Univ., Chengdu, China
Abstract :
Recently, support vector machine (SVM), which is based on statistical learning theory emerges as a hot spot in artificial intelligence. It has been widely applied in pattern recognition and many fields. A new attribute reduction method based on SVM is proposed and realized. This paper provides a new way of thinking for attribute reduction.
Keywords :
data reduction; learning (artificial intelligence); pattern recognition; rough set theory; support vector machines; SVM-based attribute reduction method; artificial intelligence; pattern recognition; statistical learning theory; support vector machine; Approximation methods; Classification algorithms; Data mining; Educational institutions; Rough sets; Support vector machines; Rough Set; attribute reduction; information systems; support vector machine;
Conference_Titel :
Natural Computation (ICNC), 2012 Eighth International Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4577-2130-4
DOI :
10.1109/ICNC.2012.6234513
