Parallel Distributed Hybrid Fuzzy GBML Models With Rule Set Migration and Training Data Rotation

We propose a parallel distributed model of a hybrid fuzzy genetics-based machine learning (GBML) algorithm to drastically decrease its computation time. Our hybrid algorithm has a Pittsburgh-style GBML framework where a rule set is coded as an individual. A Michigan-style rule-generation mechanism i...

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Veröffentlicht in:	IEEE transactions on fuzzy systems Jg. 21; H. 2; S. 355 - 368
Hauptverfasser:	Ishibuchi, H., Mihara, S., Nojima, Y.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.04.2013 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Classification algorithms Computation Computational modeling Feature extraction Fuzzy Fuzzy logic Fuzzy rule-based classifiers Fuzzy set theory Genetics genetics-based machine learning Islands Mathematical models parallel distributed algorithms Sociology Statistics Studies Training Training data training data rotation training data stratification
ISSN:	1063-6706, 1941-0034
Online-Zugang:	Volltext
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