A Bayes fusion method based ensemble classification approach for Brown cloud application
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Date
2014
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Abstract
Classification 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.