Ensemble Variable Selection for Naive Bayes to Improve Customer Behaviour Analysis  

作　　者：R.Siva Subramanian D.Prabha 

机构地区：[1]Anna University,Chennai,600025,India [2]Department of Computer Science and Engineering,Sri Krishna College of Engineering and Technology,Coimbatore,641008,India

出　　处：《Computer Systems Science & Engineering》2022年第4期339-355,共17页计算机系统科学与工程（英文）

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

关 键 词：Naive bayes or simple bayes variable selection homogeneous ensemble heterogeneous ensemble customer prediction 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]