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Title:
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EXTRACTING RULE SUBSETS IN A GENETIC ITERATIVE MODEL |
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Author(s):
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Yoel Caises , Enrique Leyva , Antonio González , Raúl Pérez |
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ISBN:
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978-972-8924-87-4 |
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Editors:
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António Palma dos Reis |
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Year:
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2009 |
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Edition:
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Single |
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Keywords:
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Machine Learning, Genetic Algorithms, Classification, Fuzzy Rules, Genetic Fuzzy Systems, Genetic Iterative Approach. |
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Type:
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Full Paper |
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First Page:
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85 |
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Last Page:
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92 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Learning fuzzy rules using genetic algorithms has proven to be a feasible way to solve high dimensionality problems. Some
researches in this area are based on the Genetic Iterative Approach, where a genetic algorithm is the main element of an
iterative covering scheme, learning one rule in each iteration. The goal of this paper is to extend the Genetic Iterative
Approach to increase the number of rules extracted in each iteration, as a way to decrease the time for learning. An
implementation of this extension is developed over a fuzzy rule-based algorithm based on the classical Genetic Iterative
Approach. It is also compared with other well-known fuzzy rule-based algorithms. |
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