A Briefest Feature Subset Selection Algorithm Based on Preference Attribute

Abstract

Concept lattice is the core data structure of formal concept analysis and represents the order relationship between the concepts iconically. Feature selection has been the focus of research in machine learning.And feature selection has been shown very effective in removing irrelevant and redundant features,also increasing efficiency in learning process and obtaining more intelligible learned results.This paper proposes a new briefest feature subset selection algorithm based on preference attribute on the basis of study of concept lattice theory. User can put forward a preference attribute according to their subjective experiences, all the briefest feature subsets containing the given attribute can be discovered by the algorithm. It firstly find some special concept pairs and calculate their waned-value hypergraph, then obtain the minimal transversal of the hypergraph as a result. A practical example proves the method is cogent and effective.