Feature Selection: Near Set Approach

The problem considered in this paper is the description of objects that are, in some sense, qualitatively near each other and the selection of features useful in classifying near objects. The term qualitatively near is used here to mean closeness of descriptions or distinctive characteristics of objects. The solution to this twofold problem is inspired by the work of Zdzis law Pawlak during the early 1980s on the classification of objects. In working toward a solution of the problem of the classification of perceptual objects, this article introduces a near set approach to feature selection. Consideration of the nearness of objects has recently led to the introduction of what are known as near sets, an optimist’s view of the approximation of sets of objects that are more or less near each other. Near set theory started with the introduction of collections of partitions (families of neighbourhoods), which provide a basis for a feature selection method based on the information content of the partitions of a set of sample objects. A byproduct of the proposed approach is a feature filtering method that eliminates features that are less useful in the classification of objects. This contribution of this article is the introduction of a near set approach to feature selection.