Abstract
This paper proposes an approach for rule base design that considers most of information obtained from a data set. The proposed method provides fuzzy rules where each consequent label has an associated weight which is determined in a probabilistic way. The paper also presents the method’s formalization using crisp and fuzzy relations and their association with the probability theory. The resulting fuzzy system is applied to five instances of a classification problem and its performance is compared to that obtained from a fuzzy classifier provided by the classical Wang-Mendel method. The results show that the proposed design method outperforms the comparison approach.
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de Melo, L.G., Lucas, L.A., Delgado, M.R. (2012). Rule-Base Design Using Probabilistic Weights: A Preliminary Analysis of Uncertainty Aspects. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances in Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 300. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31724-8_68
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DOI: https://doi.org/10.1007/978-3-642-31724-8_68
Publisher Name: Springer, Berlin, Heidelberg
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