Leafcutter Ant Colony Optimization Algorithm for Feature Subset Selection on Classifying Digital Mammograms

Abstract

Ant Colony Optimization (ACO) has been applied in wide range of applications. In ACO, for every iteration the entire problem space is considered for the solution construction using the probability of the pheromone deposits. After convergence, the global solution is made with the path which has highest pheromone deposit. In this paper, a novel solution construction technique has been proposed to reduce the time complexity and to improve the performance of the ACO. The idea is derived from the behavior of a special ant species called ‘Leafcutter Ants', they spend much of their time for cutting leaves to make fertilizer to gardens in which they grow the fungi that they eat. This behavior is incorporated with the general ACO algorithm to propose a novel feature selection method called ‘Leafcutter Ant Colony Optimization' (LACO) algorithm. The LACO has been applied to select the relevant features for digital mammograms and their corresponding classification performance is studied and compared.