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- W4253444016 abstract "The classical fuzzy classifier consists of rules each one describing one of the classes. In this paper a new fuzzy model structure is proposed where each rule can represent more than one classes with different probabilities. The obtained classifier can be considered as an extension of the quadratic Bayes classifier that utilizes mixture of models for estimating the class conditional densities. A supervised clustering algorithm has been worked out for the identification of this fuzzy model. The relevant input variables of the fuzzy classifier have been selected based on the analysis of the clusters by Fisher’s interclass separability criteria. This new approach is applied to the well-known wine and Wisconsin breast cancer classification problems." @default.
- W4253444016 created "2022-05-12" @default.
- W4253444016 date "2004-05-01" @default.
- W4253444016 modified "2023-10-16" @default.
- W4253444016 title "Editorial board" @default.
- W4253444016 doi "https://doi.org/10.1016/s0167-8655(04)00061-3" @default.
- W4253444016 hasPublicationYear "2004" @default.
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