English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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. Keywords—binary classification; artificial neural network; genetic algorithm; combined optimization; performance metric.

Combined Optimization and Modified Performance Metrics for Automated Model and Parameter Selection in Telecom Customer Churn Prediction