A Bayes fusion method based ensemble classification approach for Brown cloud application

Classification is a recurrent task of determining a target function that maps each attribute set to one of the predefined class labels. Ensemble fusion is one of the suitable classifier model fusion techniques which combine the multiple classifiers to perform high classification accuracy than individual classifiers. The main objective of this paper is to combine base classifiers using ensemble fusion methods namely Decision Template, Dempster-Shafer and Bayes to compare the accuracy of the each fusion methods on the brown cloud dataset. The base classifiers like KNN, MLP and SVM have been considered in ensemble classification in which each classifier with four different function parameters. From the experimental study it is proved, that the Bayes fusion method performs better classification accuracy of 95% than Decision Template of 80%, Dempster-Shaferof 85%, in a Brown Cloud image dataset.