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An incremental meta-cognitive-based scaffolding fuzzy neural network
journal contribution
posted on 2016-01-01, 00:00 authored by M Pratama, J Lu, S Anavatti, E Lughofer, Chee Peng LimChee Peng LimThe idea of meta-cognitive learning has enriched the landscape of evolving systems, because it emulates three fundamental aspects of human learning: what-to-learn; how-to-learn; and when-to-learn. However, existing meta-cognitive algorithms still exclude Scaffolding theory, which can realize a plug-and-play classifier. Consequently, these algorithms require laborious pre- and/or post-training processes to be carried out in addition to the main training process. This paper introduces a novel meta-cognitive algorithm termed GENERIC-Classifier (gClass), where the how-to-learn part constitutes a synergy of Scaffolding Theory - a tutoring theory that fosters the ability to sort out complex learning tasks, and Schema Theory - a learning theory of knowledge acquisition by humans. The what-to-learn aspect adopts an online active learning concept by virtue of an extended conflict and ignorance method, making gClass an incremental semi-supervised classifier, whereas the when-to-learn component makes use of the standard sample reserved strategy. A generalized version of the Takagi-Sugeno Kang (TSK) fuzzy system is devised to serve as the cognitive constituent. That is, the rule premise is underpinned by multivariate Gaussian functions, while the rule consequent employs a subset of the non-linear Chebyshev polynomial. Thorough empirical studies, confirmed by their corresponding statistical tests, have numerically validated the efficacy of gClass, which delivers better classification rates than state-of-the-art classifiers while having less complexity.
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Journal
NeurocomputingVolume
171Pagination
89 - 105Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
0925-2312eISSN
1872-8286Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2015, Crown CopyrightUsage metrics
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