Subashini, P2017-03-292017-03-292014https://ir.avinuty.ac.in/handle/avu/2302Classification is a recurrent task o f determining a target function that maps each attribute set to one o f the predefined class labels. Ensemble fusion is one o f the suitable classifier model fusion techniques which combine the multiple classifiers to perform high classification accuracy than individual classifiers. The main objective o f this paper is to combine base classifiers using ensemble fusion methods namely Decision Template, Dempster- Shafer and Bayes to compare the accuracy o f 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 fo u r different function parameters. From the experimental study it is proved, that the Bayes fusion method performs better classification accuracy o f 95% than Decision Template o f 80%, Dempster-Shaferof 85%, in a Brown Cloud image dataset.