Correlation based feature selection using quantum bio inspired estimation of distribution algorithm

Abstract

Correlation based feature Selection (CFS) evaluates different subsets based on the pairwise features correlations and the features-class correlations. Machine learning techniques are applied to CFS to help in discovering the most possible differnt combinations of features especillay in large feature spaces. This paper introduces a quantum bio inspired estimation of distribution algorithm (EDA) for CFS. The proposed algorithm integrates the quantum computing concepts, vaccination process with the immune clonal selection (QVICA) and EDA. It is employed as a search technique for CFS to find the optimal feature subset from the features space. It is implemented and evaluated using benchmark dataset KDD-cup99 and compared with the GA algorithm. The obtained results showed the ability of QVICA-with EDA to obtain better feature subsets with fewer length, higher fitness values and in a reduced computation time. © 2012 Springer-Verlag.