A Novel Approach for Feature Selection Based on Ant Colony Optimization Algorithm

A novel approach is presented to solve feature subset selection based on ACO(ant colony optimization algorithm).The approach has the ability to accommodate multiple criteria such as accuracy and cost of classification into the process of feature selection and find the effective feature subset for texture classification.A classifier based on minimum distance is described to classify two types of texture images with feature subset selected by ACO and extracted by PCA(principal component analysis) respectively.Experimental result illustrates that the algorithm can reduce feature dimension,speed the classification of image and improve the recognition rate compared to PCA.