title: |
Choquistic Regression: Generalizing Logistic Regression using the Choquet Integral |
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publication: |
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part of series: |
Advances in Intelligent Systems Research | |
| pages: | 868 - 875 | |
DOI: |
To be assigned soon (how to use a DOI) | |
author(s): |
Ali Fallah |
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publication date: |
July 2011 |
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keywords: |
logistic regression, Choquet integral,
monotone classification, attribute interaction |
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abstract: |
In this paper, we propose a generalization of logistic regression based on the Choquet integral. The
basic idea of our approach, referred to as choquistic
regression, is to replace the linear function of predictor variables, which is commonly used in logistic regression to model the log odds of the positive
class, by the Choquet integral. Thus, it becomes
possible to capture non-linear dependencies and interactions among predictor variables while preserving two important properties of logistic regression,
namely the comprehensibility of the model and the
possibility to ensure its monotonicity in individual
predictors. In experimental studies with real and
benchmark data, choquistic regression consistently
improves upon standard logistic regression in terms
of predictive accuracy. |
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copyright: |
©
Atlantis Press. This article is distributed under the
terms of the Creative Commons Attribution License, which permits
non-commercial use, distribution and reproduction in any medium,
provided the original work is properly cited. |
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full text: |