An incremental meta-cognitive-based scaffolding fuzzy neural network

Pratama, Mahardhika, Lu, Jie, Anavatti, Sreenatha, Lughofer, Edwin and Lim, Chee-Peng 2016, An incremental meta-cognitive-based scaffolding fuzzy neural network, Neurocomputing, vol. 171, pp. 89-105, doi: 10.1016/j.neucom.2015.06.022.

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Title An incremental meta-cognitive-based scaffolding fuzzy neural network
Author(s) Pratama, Mahardhika
Lu, Jie
Anavatti, Sreenatha
Lughofer, Edwin
Lim, Chee-PengORCID iD for Lim, Chee-Peng
Journal name Neurocomputing
Volume number 171
Start page 89
End page 105
Total pages 17
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-01-01
ISSN 0925-2312
Keyword(s) evolving fuzzy systems
fuzzy neural networks
meta-cognitive learning
sequential learning
Summary The 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.
Language eng
DOI 10.1016/j.neucom.2015.06.022
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Crown Copyright
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