A hybrid KNN-LR classifier and its application in customer churn prediction

Abstract

This paper presents a hybrid approach for building a binary classifier. The approach is the combination of the k-nearest neighbor algorithm, handling separately m 1-dimensional data sets divided from a data set in m-dimension, and the logistic regression method. This hybrid KNN-LR classifier improves the performance of the logistic regression in classification accuracy in some situations where the predictor and target variables exhibit complex nonlinear relationships. The results of the experiment on four benchmark data sets show the proposed approach compares favorably with the well-known classification algorithms such as C4.5 and RBF. Furthermore, its effectiveness is illustrated by its application in customer churn prediction based on real-world customer data sets.