A new class of information complexity (ICOMP) criteria with an application to customer profiling and segmentation

Abstract

This paper introduces several forms of a new class of information-theoretic measure of complexity criterion called ICOMP as a decision rule for model selection in statistical modeling to help provide new approaches relevant to statistical inference. The practical utility and the importance of ICOMP is illustrated by providing a real numerical example in data mining of mobile phone data for customer profiling and segmentation of mobile phone customers using a novel multi-class support vector machine-recursive feature elimination (MSVM-RFE) method. The approach proposed in this paper outperforms the classical discriminant analysis techniques over 32% in terms of misclassification error rate.
This is a remarkable achievement due to using MSVM-RFE hybridized with ICOMP that was not possible using other methods to classify the mobile phone customer data base as a new micro-marketing analytics. This should capture the attention of the mobile phone industry for more refined analysis of their data bases for customer management and retention.