An Experimental Evaluation of Ensemble Methods for Pattern Classification

Ensemble methods are used in many pattern recognition problems to improve the classification accuracy. Thus, in this paper, the key goal is to evaluate the performance of three popular ensemble methods bagging, boosting, and random forest for pattern recognition problems. To evaluate the performance of investigated ensemble methodology, a comparative study is realized by using three datasets taken from UCI machine learning repository. Experimental results suggest the feasibilities of ensemble classification methods, and also derived some valuable conclusions on the performance of ensemble methods for pattern classification.