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Correlation Analysis in Classifiers

Correlation Analysis in Classifiers

Vincent Lemaire, Carine Hue, Olivier Bernier
Copyright: © 2010 |Pages: 15
ISBN13: 9781605669069|ISBN10: 1605669067|EISBN13: 9781605669076
DOI: 10.4018/978-1-60566-906-9.ch011
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MLA

Lemaire, Vincent, et al. "Correlation Analysis in Classifiers." Data Mining in Public and Private Sectors: Organizational and Government Applications, edited by Antti Syvajarvi and Jari Stenvall, IGI Global, 2010, pp. 204-218. https://doi.org/10.4018/978-1-60566-906-9.ch011

APA

Lemaire, V., Hue, C., & Bernier, O. (2010). Correlation Analysis in Classifiers. In A. Syvajarvi & J. Stenvall (Eds.), Data Mining in Public and Private Sectors: Organizational and Government Applications (pp. 204-218). IGI Global. https://doi.org/10.4018/978-1-60566-906-9.ch011

Chicago

Lemaire, Vincent, Carine Hue, and Olivier Bernier. "Correlation Analysis in Classifiers." In Data Mining in Public and Private Sectors: Organizational and Government Applications, edited by Antti Syvajarvi and Jari Stenvall, 204-218. Hershey, PA: IGI Global, 2010. https://doi.org/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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