Modification of ant algorithm for Feature selection

Ant Colony Optimization (ACO) as a promising new approach to combinatorial optimization. ACO is the application of Artificial intelligence. ANT algorithm is metaheuristic used to solve combinatorial optimization problem. Ant algorithm often show good optimization behavior but are slow when compared to classical heuristics. This problem happened due to the large number of control parameters used. Feature selection is an important step in many pattern classification problems. It allows the reduction of feature space, which is reducing the training time and improving the prediction accuracy. This is achieved by removing irrelevant, redundant and noisy features. In this paper modified Ant algorithm is proposed for Feature selection and their performance is compared. Here we discuss how to evolve parameters and improve performance. This modification result in speeding up ant algorithm compared to classical one.