Ensemble Variable Selection for Naive Bayes to Improve Customer Behaviour Analysis

Executing customer analysis in a systemic way is one of the possible solutions for each enterprise to understand behavior consumer patterns an efficient and in-depth manner. Further investigation helps firm develop decisions turn, optimize enterprise’s business maximizes satisfaction correspondingly. To conduct effective assessment about customers, Naive Bayes(also called Simple Bayes), machine learning model utilized. However, efficacious simple Bayes utterly relying on data used, existence uncertain redundant attributes enables attain worst prediction because its presumption regarding applied. practice, NB premise not true data, these get poor results. In this work, ensemble attribute selection methodology performed overcome problem with pick steady uncorrelated set classifier. variable selection, two different strategies are applied: based upon perturbation (or homogeneous ensemble, same feature selector applied subsamples derived from set) other function heterogeneous utilized set). Furthermore, captured both individually outcome obtained computed. Finally, experimental outcomes show that proposed perform efficiently choosing increasing classification performance efficiently.