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.