Fast Feature Selection Using Partial Correlation for Multi-vaslued Attributes

Authors:
Stéphane Lallich;Ricco Rakotomalala
Affiliations:
-;-
Venue:
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Year:
2000

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Abstract

We propose a fast feature selection method in supervised learning for multi-valued attributes. The main idea is to rewrite the multi-valued problem in the space of examples into a boolean problem in the space of pairwise examples. On basis of this approach, we can use point correlation coefficient which is null in the case of conditional independence, and verifies a formula connecting partial coefficients with marginal coefficients. This property allows to reduce considerably the computing times because a single pass over the database is necessary to compute all coefficients. We test our algorithm on benchmark databases.