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

Yangming Zhang · Jiayin Qi · Huaying Shu · Jiantong Cao

31 citations0/0 files storedConference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics

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Customer churn and segmentation Data Mining and Machine Learning Applications Machine Learning and Data Classification

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.

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2002Credit scoring using the hybrid neural discriminant technique 2000Neural networks and the multinomial logit for brand choice modelling: a hybrid approach

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2019Churn Prediction: A Comparative Study Using KNN and Decision Trees 2017A fuzzy based churn prediction and retention model for prepaid customers in telecom industry 2010Data Mining Applications in Customer Churn Management 2021Customer determinants of used auto loan churn: comparing predictive performance using machine learning techniques 2020Parallel Programming of Churn Prediction Using Gaussian Naïve Bayes 2023Customer Churn Prediction Using Machine Learning

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