Clustering-Based Feature Selection in Semi-supervised Problems

Authors:
Ianisse Quinzán;José M. Sotoca;Filiberto Pla
Affiliations:
-;-;-
Venue:
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Year:
2009

Citing 0
Cited 2

Graph Laplacian for semi-supervised feature selection in regression problems

IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
A graph Laplacian based approach to semi-supervised feature selection for regression problems

Neurocomputing

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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.