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- W2023990410 abstract "Abstract The inductive learning of a fuzzy rule-based classification system (FRBCS) is made difficult by the presence of a large number of features that increases the dimensionality of the problem being solved. The difficulty comes from the exponential growth of the fuzzy rule search space with the increase in the number of features considered in the learning process. In this work, we present a genetic feature selection process that can be integrated in a multistage genetic learning method to obtain, in a more efficient way, FRBCSs composed of a set of comprehensible fuzzy rules with high-classification ability. The proposed process fixes, a priori, the number of selected features, and therefore, the size of the search space of candidate fuzzy rules. The experimentation carried out, using Sonar example base, shows a significant improvement on simplicity, precision and efficiency achieved by adding the proposed feature selection processes to the multistage genetic learning method or to other learning methods." @default.
- W2023990410 created "2016-06-24" @default.
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- W2023990410 date "2001-08-01" @default.
- W2023990410 modified "2023-10-14" @default.
- W2023990410 title "Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems" @default.
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- W2023990410 doi "https://doi.org/10.1016/s0020-0255(01)00147-5" @default.
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