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Even though, many researchers tried to explore the various possibilities onmulti objective feature selection, still it is yet to be explored with best ofits capabilities in data mining applications rather than going for developingnew ones. In this paper, multi-objective evolutionary algorithm ENORA is usedto select the features in a multi-class classification problem. The fusion ofAnDE (averaged n-dependence estimators) with n=1, a variant of naive Bayes withefficient feature selection by ENORA is performed in order to obtain a fasthybrid classifier which can effectively learn from big data. This method aimsat solving the problem of finding optimal feature subset from full data whichat present still remains to be a difficult problem. The efficacy of theobtained classifier is extensively evaluated with a range of most popular 21real world dataset, ranging from small to big. The results obtained areencouraging in terms of time, Root mean square error, zero-one loss andclassification accuracy.

Big Models for Big Data using Multi objective averaged one dependence estimators