A recurrent meta-cognitive-based Scaffolding classifier from data streams

Pratama, M; Lu, J; Anavatti, SG; Iglesias, JA

A recurrent meta-cognitive-based Scaffolding classifier from data streams

Pratama, M Lu, J

Anavatti, SG Iglesias, JA

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Publication Type:: Conference Proceeding
Citation:: IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings, 2014, pp. 132 - 139
Issue Date:: 2014-01-13

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Field	Value	Language
dc.contributor.author	Pratama, M	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Anavatti, SG	en_US
dc.contributor.author	Iglesias, JA	en_US
dc.date.issued	2014-01-13	en_US
dc.identifier.citation	IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings, 2014, pp. 132 - 139	en_US
dc.identifier.isbn	9781479944958	en_US
dc.identifier.uri	http://hdl.handle.net/10453/35306
dc.identifier.uri	http://hdl.handle.net/10453/34983
dc.identifier.uri	http://hdl.handle.net/10453/35305
dc.description.abstract	© 2014 IEEE. A novel incremental meta-cognitive-based Scaffolding algorithm is proposed in this paper crafted in a recurrent network based on fuzzy inference system termed recurrent classifier (rClass). rClass features a synergy between schema and scaffolding theories in the how-to-learn part, which constitute prominent learning theories of the cognitive psychology. In what-to-learn component, rClass amalgamates the new online active learning concept by virtue of the Bayesian conflict measure and dynamic sampling strategy, whereas the standard sample reserved strategy is incorporated in the when-to-learn constituent. The inference scheme of rClass is managed by the local recurrent network, sustained by the generalized fuzzy rule. Our thorough empirical study has ascertained the efficacy of rClass, which is capable of producing reliable classification accuracies, while retaining the amenable computational and memory burdens.	en_US
dc.relation.ispartof	IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings	en_US
dc.relation.isbasedon	10.1109/EALS.2014.7009514	en_US
dc.title	A recurrent meta-cognitive-based Scaffolding classifier from data streams	en_US
dc.type	Conference Proceeding
utslib.for	080105 Expert Systems	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	080605 Decision Support and Group Support Systems	en_US
dc.location.activity	Orlando, Florida, USA
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. A novel incremental meta-cognitive-based Scaffolding algorithm is proposed in this paper crafted in a recurrent network based on fuzzy inference system termed recurrent classifier (rClass). rClass features a synergy between schema and scaffolding theories in the how-to-learn part, which constitute prominent learning theories of the cognitive psychology. In what-to-learn component, rClass amalgamates the new online active learning concept by virtue of the Bayesian conflict measure and dynamic sampling strategy, whereas the standard sample reserved strategy is incorporated in the when-to-learn constituent. The inference scheme of rClass is managed by the local recurrent network, sustained by the generalized fuzzy rule. Our thorough empirical study has ascertained the efficacy of rClass, which is capable of producing reliable classification accuracies, while retaining the amenable computational and memory burdens.

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http://hdl.handle.net/10453/34983