A bipolar knowledge representation model to improve supervised fuzzy classification algorithms
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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.
KeywordsSupervised classification models Bipolar models Machine learning Soft information
This research has been partially supported by the Government of Spain, Grant TIN2015-66471-P and the FPU fellowship Grant 2015/06202 from the Ministry of Education of Spain.
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Conflict of interest
The authors declare that they have no conflict of interest.
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