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

Hamparsun Bozdogan
1.591 771


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.


ICOMP class of criteria, covariance complexity, estimated inverse-Fisher information matrix (FIM), model selection, multi-class support vector machine-recursive feature elimination (MSVM-RFE), customer profiling and segmentation

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