Fast feature selection using partial correlation for multi-valued attributes

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.