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A hybrid FAM-CART model for online data classification
journal contribution
posted on 2018-05-01, 00:00 authored by M Seera, Chee Peng LimChee Peng Lim, S C Tan© 2018 Wiley Periodicals, Inc. In this paper, an online soft computing model based on an integration between the fuzzy ARTMAP (FAM) neural network and the classification and regression tree (CART) for undertaking data classification problems is presented. Online FAM network is useful for conducting incremental learning with data samples, whereas the CART model prevails in depicting the knowledge learned explicitly in a tree structure. Capitalizing on their respective advantages, the hybrid FAM-CART model is capable of learning incrementally while explaining its predictions with knowledge elicited from data samples. To evaluate the usefulness of FAM-CART, 2 sets of benchmark experiments with a total of 12 problems are used in both offline and online learning modes. The results are examined and compared with those published in the literature. The experimental outcome positively indicates that the online FAM-CART model is useful for tackling data classification tasks. In addition, a decision tree is produced to allow users in understanding the predictions, which is an important property of the hybrid FAM-CART model in supporting decision-making tasks.
History
Journal
Computational intelligenceVolume
34Issue
2Pagination
562 - 581Publisher
WileyLocation
Chichester, Eng.Publisher DOI
ISSN
0824-7935eISSN
1467-8640Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2018, Wiley PeriodicalsUsage metrics
Categories
Keywords
Science & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Scienceclassification and regression treedata classificationfuzzy ARTMAPonline learningrule extractionNEURAL-NETWORKFAULT-DETECTIONALGORITHMSYSTEMSInformation SystemsArtificial Intelligence and Image ProcessingComputation Theory and Mathematics
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