Improved UTA Feature Selection using Ant Colony Optimization

Abstract

Feature Selection is the problem of choosing a minimal subset from the set of all features which are sufficient and necessary for classifier. UTA (1) is a simple algorithm performed in the trained artificial neural network. UTA evaluates the features according to their accuracy by removing them one by one. This algorithm classifies features into three categories: relevant, irrelevant and redundant features. UTA can guarantees that all of the relevant features are useful, but the disadvantage of UTA is that all correlated features will be determined as irrelevant/redundant features; because they are evaluated one by one. But in fact some of them may be relevant features. Ant colony optimization (ACO) is widely used for feature selection, and has very good performance; but it needs to a reasonable running time. In this paper at the beginning a UTA algorithm is performed, and then an ACO is used for finding those useful features which UTA could not find them. Proposed algorithm (called UTAACO) efficiently improved the performance of UTA, and reduced the computational time of ACO. Obtained results indicate the robustness of UTAACO.