A Robust Ensemble Based Approach to Combine Heterogeneous Classifiers in the Presence of Class Label Noise

Abstract

In this paper, we introduced a classifier ensemble approach to combine heterogeneous classifiers in the presence of class label noise in the datasets. To enhance the performance of classifier ensemble, we give a preprocessing approach to filter out this class label noise. The filtered data is then used to learn individual classifier model. After that, a weight learning method is introduced to learn weights on each individual classifier to create a classifier ensemble. We applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give best accuracy. The proposed approach is evaluated on variety of real life datasets. The proposed technique is also compared with existing standard ensemble techniques such as Adaboost, Bagging and RSM to show the superiority of proposed ensemble method, in the presence of class label noise, as compared to its competitors and also to show the sensitivity of competitors to class label noise.