Rough sets and evolutionary optimization Algorithms for feature selection in mammograms

摘要

Breast cancer detection techniques have been reported to aid radiologists in analyzing mammograms. In this paper new feature selection strategy based on rough sets and particle swarm optimization (PSO) and New particle swarm optimization (NPSO) has been proposed. Rough sets have been used as a feature selection method with much success, but current hillclimbing rough set approaches to feature selection are inadequate at finding optimal reductions as no perfect heuristic can guarantee optimality. Like Genetic Algorithms, PSO is a new evolutionary computation technique, in which each potential solution is seen as a particle with a certain velocity flying through the problem space. The Particle Swarms find optima regions of the complex search space through the interaction of individuals in the population. PSO is attractive for feature selection in that particle swarms will discover best feature combinations as they fly within the subset space. We have also introduced a New particle swarm optimization (NPSO). Experimentation is carried out. using MIAS data, which compares the proposed algorithm with a GA-based approach. The results show that PSO and NPSO are efficient for rough set-based feature selection.