Combined Optimization and Modified Performance Metrics for Automated Model and Parameter Selection in Telecom Customer Churn Prediction
Stanislav Alkhasov, Alexey Tselykh
Available Online December 2017.
- https://doi.org/10.2991/itsmssm-17.2017.42How to use a DOI?
- binary classification, artificial neural network, genetic algorithm, combined optimization, performance metric.
- - In this paper, we consider the construction of an optimization algorithm designed to identify, in automatic way, the optimal parameters and algorithms for binary classification in the task of customer churn prediction in telecommunication. The currently supported methods for classification include Decision Trees, k-Nearest Neighbors, Support Vector Machines, and Back-Propagation Artificial Neural Networks. It is shown that in certain cases the standard textbook metrics of classification model quality (e.g. accuracy, precision, recall, and AUC) are not descriptive enough. Thus, we evaluate the algorithms using the modified recall metrics: Weighted Recall metric, and Weighted Recall and Run-Time metric. These metrics are appropriate for use in automatic mode without continuous expert supervision. We use genetic algorithms as optimization algorithms. To improve standard implementations, we introduce a combined optimization approach based on D.H. de Vries model of disaster evolution and the island model.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Stanislav Alkhasov AU - Alexey Tselykh PY - 2017/12 DA - 2017/12 TI - Combined Optimization and Modified Performance Metrics for Automated Model and Parameter Selection in Telecom Customer Churn Prediction BT - IV International research conference "Information technologies in Science, Management, Social sphere and Medicine" (ITSMSSM 2017) PB - Atlantis Press SP - 196 EP - 200 SN - 2352-538X UR - https://doi.org/10.2991/itsmssm-17.2017.42 DO - https://doi.org/10.2991/itsmssm-17.2017.42 ID - Alkhasov2017/12 ER -