Title :
Clustering-Based Feature Selection in Semi-supervised Problems
Author :
Quinzan, I. ; Sotoca, José M. ; Pla, Filiberto
Author_Institution :
Dept. Llenguatges i Sistemes Inf., Univ. Jaume I, Castellon de la Plana, Spain
fDate :
Nov. 30 2009-Dec. 2 2009
Abstract :
In this contribution a feature selection method in semi-supervised problems is proposed. This method selects variables using a feature clustering strategy, using a combination of supervised and unsupervised feature distance measure, which is based on conditional mutual information and conditional entropy. Real databases were analyzed with different ratios between labelled and unlabelled samples in the training set, showing the satisfactory behaviour of the proposed approach.
Keywords :
entropy; learning (artificial intelligence); pattern clustering; clustering-based feature selection; conditional entropy; conditional mutual information; feature clustering; semisupervised learning; unsupervised feature distance measure; Clustering algorithms; Data analysis; Entropy; Filters; Intelligent systems; Labeling; Mutual information; Programmable logic arrays; Semisupervised learning; Spatial databases; Semi-supervised learning; feature selection; information measures;
Conference_Titel :
Intelligent Systems Design and Applications, 2009. ISDA '09. Ninth International Conference on
Conference_Location :
Pisa
Print_ISBN :
978-1-4244-4735-0
Electronic_ISBN :
978-0-7695-3872-3
DOI :
10.1109/ISDA.2009.211
