Transfer Learning and Meta Classification Based Deep Churn Prediction System for Telecom Industry

TL;DR

This paper introduces TL-DeepE, a novel churn prediction system for telecom industry that combines transfer learning with ensemble meta-classification, converting vector data into images for deep CNNs and improving prediction accuracy.

Contribution

The paper proposes a new two-stage method integrating transfer learning with deep CNNs and ensemble meta-classification for improved churn prediction in telecom datasets.

Findings

Achieved 75.4% accuracy on Orange dataset

Attained 68.2% accuracy on Cell2cell dataset

Improved AUC scores over existing techniques

Abstract

A churn prediction system guides telecom service providers to reduce revenue loss. However, the development of a churn prediction system for a telecom industry is a challenging task, mainly due to the large size of the data, high dimensional features, and imbalanced distribution of the data. In this paper, we present a solution to the inherent problems of churn prediction, using the concept of Transfer Learning (TL) and Ensemble-based Meta-Classification. The proposed method TL-DeepE is applied in two stages. The first stage employs TL by fine-tuning multiple pre-trained Deep Convolution Neural Networks (CNNs). Telecom datasets are normally in vector form, which is converted into 2D images because Deep CNNs have high learning capacity on images. In the second stage, predictions from these Deep CNNs are appended to the original feature vector and thus are used to build a final feature vector for the high-level Genetic Programming (GP) and AdaBoost based ensemble classifier. Thus, the experiments are conducted using various CNNs as base classifiers and the GP-AdaBoost as a meta-classifier. By using 10-fold cross-validation, the performance of the proposed TL-DeepE system is compared with existing techniques, for two standard telecommunication datasets; Orange and Cell2cell. Performing experiments on Orange and Cell2cell datasets, the prediction accuracy obtained was 75.4% and 68.2%, while the area under the curve was 0.83 and 0.74, respectively.