Two Floating Search Strategies to Compute the Support Sets System for ALVOT

In this paper, two strategies to compute the support sets system for the supervised classifier ALVOT (voting algorithms) using sequential floating selection are presented. ALVOT is a supervised classification model based on the partial precedence principle, therefore, it needs, as feature selection, a set of features subsets, this set is called support sets system. The sequential floating selection methods for feature selection find only one relevant features subset. The introduced strategies search for a set of features subsets to generate a support sets system. Both strategies are compared between them and against the feature selection method based on testor theory, which is commonly used to compute this system. Results obtained with both strategies on different databases from UCI and on the faces database from Olivetti Research Laboratory (ORL) in Cambridge are presented.