Improved Accuracy of Naive Bayes Classifier for Determination of Customer Churn Uses SMOTE and Genetic Algorithms

Abstract

With increasing competition in the business world, many companies use data mining  techniques to determine the level of customer loyalty. The customer data used in this  study is the german credit dataset obtained from UCI. Such data have an imbalance  problem of class because the amount of data in the loyal class is more than in the  churn class. In addition, there are some irrelevant attributes for customer  classification, so attributes selection is needed to get more accurate classification  results. One classification algorithm is naive bayes. Naive Bayes has been used as an  effective classification for years because it is easy to build and give an independent  attribute into its structure. The purpose of this study is to improve the accuracy of the  Naive Bayes for customer classification. SMOTE and genetic algorithm do for  improving the accuracy. The SMOTE is used to handle class imbalance problems,  while the genetic algorithm is used for attributes selection. Accuracy using the Naive  Bayes is 47.10%, while the mean accuracy results obtained from the Naive Bayes  with the application of the SMOTE is 78.15% and the accuracy obtained from the  Naive Bayes with the application of the SMOTE and genetic algorithm is 78.46%.