DocumentCode :
506857
Title :
A Novel Algorithm Based on Conditional Entropy Established by Clustering for Feature Selection
Author :
Yang, Ping ; Yang, Ming
Author_Institution :
Sch. of Math. Sci., Nanjing Normal Univ. Nanjing, Nanjing, China
Volume :
1
fYear :
2009
fDate :
14-16 Aug. 2009
Firstpage :
410
Lastpage :
415
Abstract :
Feature selection is an important issue in machine learning. Rough set theory is one of the important methods for feature selection. In rough set theory, feature selection has already been separately studied in algebra view and information view. Unfortunately, the previously proposed methods based on information entropy for feature selection only focus on the discrete datasets. However, how to effectively discretize the continuous datasets is also full of challenge, since this method may lead to loss of some useful information. To overcome this disadvantage, in this paper, we introduce a novel algorithm based on conditional entropy by clustering strategy for feature selection (ACECFS). In ACECFS, the projected data corresponding to each feature is appropriately separated into several clusters at first, and then the conditional entropy for a set of features is conveniently computed by the clusters and corresponding feature list is generated, hence an effectively relevant and compact feature subset can be obtained from the ranked feature list. Experiments show the effectiveness of ACECFS.
Keywords :
learning (artificial intelligence); pattern clustering; rough set theory; compact feature subset; conditional entropy; feature selection clustering; information entropy; machine learning; rough set theory; Algebra; Clustering algorithms; Entropy; Feature extraction; Filters; Fuzzy systems; Machine learning; Machine learning algorithms; Pattern recognition; Set theory; Clustering; Conditional Entropy; Feature Selection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3735-1
Type :
conf
DOI :
10.1109/FSKD.2009.92
Filename :
5358553
Link To Document :
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