Abstract
IRBASIR is a recently proposed algorithm that, inspired on one of the extensions of classical Rough Set Theory, employs similarity relations to learn classification rules. By using similarity relations as its underlying building blocks, IRBASIR is able to process datasets with both nominal and numerical features. In this paper we propose IRBASIR-Bayes, a modification to the IRBASIR method that relies on Bayesian Networks to construct the reference vector used to generate the rules. This scheme has demonstrated satisfactory performance compared to other rule induction algorithms.
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Coello, L., Filiberto, Y., Bello, R., Frias, M., Falcon, R. (2020). IRBASIR-B: Rule Induction from Similarity Relations, a Bayesian Approach. In: Figueroa-García, J.C., Garay-Rairán, F.S., Hernández-Pérez, G.J., Díaz-Gutierrez, Y. (eds) Applied Computer Sciences in Engineering. WEA 2020. Communications in Computer and Information Science, vol 1274. Springer, Cham. https://doi.org/10.1007/978-3-030-61834-6_3
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DOI: https://doi.org/10.1007/978-3-030-61834-6_3
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