Optimal Classifier Ensemble Design Based on Cooperative Game Theory
Jafar Ahmad Abed Alzubi · Research Journal of Applied Sciences Engineering and Technology · 2015
Classifier ensemble techniques have been an active area of machine learning research in recent years. The aim of combining classifier ensembles is to improve the accuracy of the ensemble compared to any individual classifier. An ensemble can overcome the weakness of an individual classifier if its base classifiers do not make simultaneous errors. In this study, a novel algorithm for optimal classifier ensemble design called Coalition-based Ensemble Design (CED) is proposed and studied in detail. The CED algorithm aims to reduce the size and the generalization error of a classifier ensemble while improving accuracy. The underlying theory is based on the formation of coalitions in cooperative game theory. The algorithm estimates the diversity of an ensemble using the Kappa Cohen measure for multi base classifiers and selects a coalition based on their contributions to overall diversity. The CED algorithm is compared empirically with several classical design methods, namely Classic ensemble, Clustering, Thinning and Most Diverse algorithms. Experimental results show that the CED algorithm is superior in creating the most diverse and accurate classifier ensembles.