Weighted reduct selection metaheuristic based approach for rules reduction and visualization

Rules dimensionality may hinder the promising benefits of any automatic diagnostic approach. Recent studies pointed out toward meta-heuristics techniques due to their fast processing and promising results. In this paper, a hybrid approach combines the Rough Set Theory (RS) and the metaheuristic Genetic Algorithm (GA) to reach an efficient classifier of Breast Cancer disease with reduced rules. The proposed approach is called Rough set with Genetic Algoritm (RSGA). RS was used in RSGA for feature selection process, the reduct selection was performed using a weighted function based on the minimal mean square error of each reduct. The GA works for reducing extracted rules from the selected reduct. Microsoft Automatic Graph Layout (MSAGL) was used for visualizing reduced rules. The performance of the hybrid approach outperforms the results extracted from RS classifier and was evaluated using accuracy, specificity and sensitivity measures. RSGA achieved the highest performance for accuracy reached 98% and solved the problem of rules dimensionality and enabled visual information.