Induction of strong feature subsets

Abstract

The problem of features subset selection can be defined as the selection of a relevant subset of features which allows a learning algorithm to induce small high-accuracy concepts. To achieve this goal, two main approaches have been developed, the first one is algorithm-independent (filter approach) which considers only the data, when the second approach takes into account both the data and a given learning algorithm (wrapper approach). Recent work were developed to study the interest of rough set theory and more particularly its notions of reducts and core to deal with the problem of features subset selection. Different methods were proposed to select features using both the core and the reduct concepts, whereas other researches show that useful features subset does not necessarily contain all features in cores. In this paper, we underline the fact that rough set theory is concerned with deterministic analysis of attribute dependencies which are at basis of the two notions of reduct and core. We extend the notion of dependency which allows us to find both deterministic and non-deterministic dependencies. A new notion of strong reducts is then introduced and leads to the definition of strong feature subsets (SFS). The interest of SFS is illustrated by the improvement of the accuracy of our learning system, called Alpha, on real-world datasets.