Churn Prediction Using Mathematical Programming via a Linear Classification Algorithm

Q3 Mathematics
Mohamed Barhdadi, Badreddine Benyacoub, Mohamed Ouzineb
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引用次数: 0

Abstract

The initial idea in developing customer churn prediction was to use statistical analysis of a sample of previous customers to help operators predict which existing customers were likely migrate to other operators. In this paper, a nonstatistical approach to this problem can also be envisaged by formulating the problem as a linear program. The main concept of our proposed algorithm is to hybridize the Jackknife resampling technique with an efficient heuristic based on a variable neighborhood search algorithm to construct a new approach that can present an alternative to black-box machine learning methods. The goal is to find possible solutions (or weights) to minimize error distances and the number of misclassified points. We tested the suggested algorithm with an imbalanced instance and the comparisons show that the proposed model outperforms the most existing machine learning classifiers, both in terms of the solution quality and the execution time. The numerical results indicate that the best solution is achieved in 85% over G-mean of all instances tested. The average gap between our solution and the best solution is still quite small (1.15%).

Abstract Image

通过线性分类算法使用数学编程进行流失预测
摘要 开发客户流失预测的最初想法是利用对以往客户样本的统计分析,帮助运营商预测哪些现有客户可能会迁移到其他运营商。在本文中,通过将该问题表述为线性程序,还可以设想一种非统计方法来解决该问题。我们提出的算法的主要概念是将积刀重采样技术与基于变量邻域搜索算法的高效启发式算法相结合,从而构建出一种可替代黑盒机器学习方法的新方法。我们的目标是找到可能的解决方案(或权重),使误差距离和误分类点的数量最小化。我们用不平衡实例测试了所建议的算法,比较结果表明,所建议的模型在解决方案质量和执行时间方面都优于大多数现有的机器学习分类器。数值结果表明,在所有测试实例中,85% 的 G-mean 都能获得最佳解决方案。我们的解决方案与最佳解决方案之间的平均差距仍然很小(1.15%)。
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来源期刊
Mathematical Models and Computer Simulations
Mathematical Models and Computer Simulations Mathematics-Computational Mathematics
CiteScore
1.20
自引率
0.00%
发文量
99
期刊介绍: Mathematical Models and Computer Simulations  is a journal that publishes high-quality and original articles at the forefront of development of mathematical models, numerical methods, computer-assisted studies in science and engineering with the potential for impact across the sciences, and construction of massively parallel codes for supercomputers. The problem-oriented papers are devoted to various problems including industrial mathematics, numerical simulation in multiscale and multiphysics, materials science, chemistry, economics, social, and life sciences.
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