An incremental meta-cognitive-based scaffolding fuzzy neural network
Data(s) |
01/01/2016
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Resumo |
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. |
Identificador | |
Idioma(s) |
eng |
Publicador |
Elsevier |
Relação |
http://dro.deakin.edu.au/eserv/DU:30076122/lim-incrementalmetacognitive-2016.pdf http://www.dx.doi.org/10.1016/j.neucom.2015.06.022 |
Direitos |
2015, Crown Copyright |
Palavras-Chave | #evolving fuzzy systems #fuzzy neural networks #meta-cognitive learning #sequential learning |
Tipo |
Journal Article |