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A bipolar knowledge representation model to improve supervised fuzzy classification algorithms.

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Villarino, Guillermo and Gomez, Daniel and Rodríguez, Juan Tinguaro and Montero, Javier (2018) A bipolar knowledge representation model to improve supervised fuzzy classification algorithms. Soft Computing . pp. 1-26. ISSN 1432-7643

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Official URL: https://link.springer.com/article/10.1007/s00500-018-3320-9




Abstract

Most supervised classification algorithms produce a soft score (either a probability, a fuzzy degree, a possibility, a cost, etc.) assessing the strength of the association between items and classes. After that, each item is assigned to the class with the highest soft score. In this paper, we show that this last step can be improved through alternative procedures more sensible to the available soft information. To this aim, we propose a general fuzzy bipolar approach that enables learning how to take advantage of the soft information provided by many classification algorithms in order to enhance the generalization power and accuracy of the classifiers. To show the suitability of the proposed approach, we also present some computational experiences for binary classification problems, in which its application to some well-known classifiers as random forest, classification trees and neural networks produces a statistically significant improvement in the performance of the classifiers.


Item Type:Article
Uncontrolled Keywords:Supervised classification models; Bipolar models; Machine learning; Soft information
Subjects:Sciences > Mathematics > Probabilities
ID Code:48330
Deposited On:29 Jun 2018 08:20
Last Modified:03 Jul 2018 07:47

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