  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.
