Classifier Ensemble Selection Based on mRMR Algorithm and Diversity Measures: An Application of Medical Data Classification

Classifier selection is a significant problem in machine learning to reduce the computational time and the number of ensemble members. Over the past decade, multiple classifier systems (MCS) have been actively exploited to enhance the classification accuracy. Finding a pertinent objective function for measuring the competence of base classifier is a critical issue to select the appropriate subset from a pool of classifiers. Along with the accuracy, diversity measures are designed as objective functions for ensemble selection. This current work proposed a new selection method based on accuracy and diversity in order to achieve better medical data classification performance. The classifiers correlation was calculated using Minimum Redundancy Maximum Relevance (mRMR) method based on relevance and diversity measures. Experiments were carried out on five data sets from UCI Machine Learning Repository and LudmilaKuncheva Collection. The experimental results proved the superiority of the proposed classifiers selection method.