Title :
Classification rule mining using feature selection and genetic algorithm
Author :
Li, Xin ; Qian, Xu ; Wang, Ziqiang
Author_Institution :
Coll. of Mech. Electron. & Inf. Eng., China Univ. of Min. & Technol., Beijing, China
Abstract :
Classification rule mining has been a very active research topic in data mining and machine learning communities. To effectively cope with this problem, a novel classification rule mining algorithm is proposed by the combination of neighborhood preserving embedding (NPE) and genetic algorithm (GA) in this paper. Experimental results on the UCI data set repository demonstrate that the proposed algorithm performs much better than other well-known classification rule mining algorithms.
Keywords :
data mining; feature extraction; genetic algorithms; learning (artificial intelligence); UCI data set repository; classification rule mining; data mining; feature selection; genetic algorithm; machine learning; neighborhood preserving embedding; Classification algorithms; Data engineering; Data mining; Educational institutions; Feature extraction; Genetic algorithms; Industrial electronics; Linear discriminant analysis; Machine learning algorithms; Principal component analysis; classification rule; data mining; feature selection; genetic algorithm;
Conference_Titel :
Computational Intelligence and Industrial Applications, 2009. PACIIA 2009. Asia-Pacific Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4606-3
DOI :
10.1109/PACIIA.2009.5406606
