Enhanced Ant Colony Algorithm for Best Features Selection for a Decision Tree Classification of Medical Data

Abstract

Classification algorithms are widely applied in medical domain to classify the data for diagnosis. The datasets have considerable irrelevant attributes. Diagnosis of the diseases is costly because many tests are required to predict a disease. Feature selection is one of the significant tasks of the preprocessing phase for the data. It can extract a subset of attributes from a large set and exclude redundant, irrelevant, or noisy attributes. The authors can decrease the cost of diagnosis by avoiding numerous tests by selection of features, which are important for prediction of disease. Applied to the task of supervised classification, the authors construct a robust learning model for disease prediction. The search for a subset of features is an NP-hard problem, which can be solved by the metaheuristics. In this chapter, a wrapper approach by hybridization between ant colony algorithm and adaboost with decision trees to ameliorate the classification is proposed. The authors use an enhanced global pheromone updating rule. With the experimental results, this approach gives good results.