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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Acknowledgment
The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.
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Baati, K., Hamdani, T.M., Alimi, A.M., Abraham, A. (2017). A Modified Naïve Possibilistic Classifier for Numerical Data. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_41
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DOI: https://doi.org/10.1007/978-3-319-53480-0_41
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