A modified naïve possibilistic classifier for numerical data

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Abstract

In this paper, we propose a modified version of the Naïve Possibilistic Classifier (NPC) which has been already suggested to make decision from numerical data. As the former NPC, the modified classifier makes use of the probability to possibility transformation of Dubois et al. in the continuous case in order to estimate possibilistic beliefs. However, unlike the former NPC which uses the product as a fusion operator, the proposed classifier fuses possibilistic beliefs using the generalized minimum-based algorithm which has been recently proposed as an improvement of the minimum operator for combining possibilistic estimates. Experimental evaluations are conducted on 15 numerical datasets taken from University of California Irvine (UCI) and show that the new version of NPC largely outperforms the former one in terms of accuracy.

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Baati, K., Hamdani, T. M., Alimi, A. M., & Abraham, A. (2017). A modified naïve possibilistic classifier for numerical data. In Advances in Intelligent Systems and Computing (Vol. 557, pp. 417–426). Springer Verlag. https://doi.org/10.1007/978-3-319-53480-0_41

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