A feature selection algorithm based on heuristic decomposition

Abstract

Feature selection is one of the most important concepts in data mining when dimensionality reduction is needed. The performance measures of feature selection encompass predictive accuracy and result comprehensibility. Consistency based feature selection is a significant category of feature selection research that substantially improves the comprehensibility of the result using the parsimony principle. In this work, the feature selection algorithm LAID, Logical Analysis of Inconsistent Data, is applied to large volumes of data. In order to deal with hundreds of thousands of attributes, a problem de-composition strategy associated with a set covering problem formulation is used. The algorithm is applied to artificial datasets with genome-like characteristics of patients with rare diseases.