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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
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
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Article-2
ObjectType-Feature-1
content type line 23
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2012.2215331