A New Hybrid Method for Feature Selection using Ant Colony Optimization and Firefly Algorithm

In high-dimensional feature space, unrelated features decrease the accuracy of classification and increase the computational complexity. The Selection of feature provides the relevant and useful information and improves the efficiency. The Selection of feature can be seen as an optimization problem because the selection of appropriate subset of features is very important. This paper presents a novel feature selection algorithm based on combination of Ant Colony optimization and firefly algorithm, called FFACO to optimize the selection of features. The Ant Colony algorithm is a famous meta-heuristic search algorithm used in solving combinatorial optimization problems. Firefly algorithm is an evolutionary model based on collective intelligence algorithms and derived from nature. This algorithm is mainly used in solving optimization problem. Eight UCI datasets and three classifier learning algorithm have been used for evaluating the proposed algorithm. Experimental results show that proposed algorithm (FFACO), increases classification accuracy, by selecting the least number of features, in most instances

Keywords:

Feature Selection , Classification , Firefly Algorithm , Ant Colony Algorithm