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Correlation Analysis in Classifiers

Correlation Analysis in Classifiers
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Author(s): Vincent Lemaire (France Télécom, France), Carine Hue (GFI Informatique, France) and Olivier Bernier (France Télécom, France)
Copyright: 2010
Pages: 15
Source title: Data Mining in Public and Private Sectors: Organizational and Government Applications
Source Author(s)/Editor(s): Antti Syvajarvi (University of Lapland, Finland) and Jari Stenvall (Tampere University, Finland)
DOI: 10.4018/978-1-60566-906-9.ch011

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Abstract

This chapter presents a new method to analyze the link between the probabilities produced by a classification model and the variation of its input values. The goal is to increase the predictive probability of a given class by exploring the possible values of the input variables taken independently. The proposed method is presented in a general framework, and then detailed for naive Bayesian classifiers. We also demonstrate the importance of “lever variables”, variables which can conceivably be acted upon to obtain specific results as represented by class probabilities, and consequently can be the target of specific policies. The application of the proposed method to several data sets shows that such an approach can lead to useful indicators.

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