核极端学习机支持的客户流失预测财务风险评估模型

U. Dimlo, Rosario Huerta-Soto, Laura Nivin-Vargas, John Tarazona-Jiménez, Carla Reyes-Reyes, N. Girdharwal
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引用次数: 0

摘要

目前,客户关系管理(CRM)方法以个性化的营销习惯取代了经典的大众营销原则。客户关系管理主要集中于有效用户,这有助于做出最佳决策。为了控制客户流失,有必要建立成功的、准确的用户流失预测(CCP)方法。利用数据挖掘和统计方法建立了客户流失预测方法。提供了几种DM技术来检测被称为流失的忠实用户。CCP被认为是决策者和机器学习(ML)社会的一个复杂过程,因为很多时候,非流失客户和流失客户添加了类似的功能。因此,本文提出了一种核化极限学习机支持CCP (KELMCCP)模型。提出的KELMCCP模型旨在利用客户数据有效地确定是否存在流失。为了实现这一点,KELMCCP模型采用KELM分类模型,KELM是ELM模型的扩展类型。KELM模型采用具有任意隐节点的增量建设性前馈网络进行全局逼近。在这里,KELM模型的参数调整使用细菌觅食优化(BFO)算法进行。KELM模型的使用有助于提高分类性能。为了证明KELMCCP模型的优越结果,我们进行了广泛的实验分析,并从多个方面对结果进行了检验。仿真结果保证了KELMCCP模块比现有方法具有更高的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Kernelized Extreme Learning Machine Enabled Churn Predictive Financial Risk Assessment Model
Currently, Customer Relationship Management (CRM) methodologies replace the classical mass marketing principles with personalized marketing habits. The CRM primarily concentrates on effectual users which aids in making optimal decisions. For controlling customer churn, it becomes necessary to build successful and précised user customer churn prediction (CCP) method. The data mining (DM) and statistical methodologies were implied to create a churn prediction approach. Several DM techniques were provided for detecting loyal subscribers that are well-known as churn. CCP is regarded as a complicated procedure for decision makers and machine learning (ML) society because many times, non-churn and churn customers add similar features. Therefore, this paper presents a Kernelized Extreme Learning Machine Enabled CCP (KELMCCP) model. The presented KELMCCP model intends to effectually determine the existence and non-existence of churns using customer data. To perform this, the KELMCCP model employs the KELM classification model which is an extended type of ELM model. The KELM model follows global approximation by the use of incremental constructive feed-forward network with arbitrary hidden nodes. Here, the parameter tuning of the KELM model takes place using the bacterial foraging optimization (BFO) algorithm. The use of KELM model helps in accomplishing enhanced classification performance. In order to demonstrate the superior outcomes of the KELMCCP model, a wide-ranging experiment analysis is carried out and the outcomes are inspected in numerous aspects. The simulation results ensured the higher performance of the KELMCCP module over recent approaches.
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