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Author Quinzan, I. ♦ Sotoca, J.M. ♦ Pla, F.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2009
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Data analysis ♦ Spatial databases ♦ Entropy ♦ Filters ♦ Semi-supervised learning ♦ Clustering algorithms ♦ Programmable logic arrays ♦ Semisupervised learning ♦ information measures ♦ Labeling ♦ Intelligent systems ♦ Mutual information ♦ feature selection
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.
ISBN 9781424447350
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2009-11-30
Publisher Place Italy
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 394.68 kB
Page Count 6
Starting Page 535
Ending Page 540

Source: IEEE Xplore Digital Library