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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Vydané v:	IEEE transactions on fuzzy systems Ročník 21; číslo 2; s. 355 - 368
Hlavní autori:	Ishibuchi, H., Mihara, S., Nojima, Y.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.04.2013 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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Abstract	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 is used as a kind of local search. Our parallel distributed model is an island model where a population of individuals is divided into multiple islands. Training data are also divided into multiple subsets. The main feature of our model is that a different training data subset is assigned to each island. The assigned training data subsets are periodically rotated over the islands. The best rule set in each island also migrates periodically. We demonstrate through computational experiments that our model decreases the computation time of the hybrid fuzzy GBML algorithm by an order or two of magnitude using seven parallel processors without severely degrading the generalization ability of obtained fuzzy rule-based classifiers. We also examine the effects of the training data rotation and the rule set migration on the search ability of our model.
AbstractList	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 is used as a kind of local search. Our parallel distributed model is an island model where a population of individuals is divided into multiple islands. Training data are also divided into multiple subsets. The main feature of our model is that a different training data subset is assigned to each island. The assigned training data subsets are periodically rotated over the islands. The best rule set in each island also migrates periodically. We demonstrate through computational experiments that our model decreases the computation time of the hybrid fuzzy GBML algorithm by an order or two of magnitude using seven parallel processors without severely degrading the generalization ability of obtained fuzzy rule-based classifiers. We also examine the effects of the training data rotation and the rule set migration on the search ability of our model.
Author	Mihara, S. Nojima, Y. Ishibuchi, H.
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SubjectTerms	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
Title	Parallel Distributed Hybrid Fuzzy GBML Models With Rule Set Migration and Training Data Rotation
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