A Recurrent Meta-Cognitive-Based Scaffolding Classifier from Data Streams

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
IEEE Symposium on Evolving and Automomous Learning, 2014, pp. 132 - 139
Issue Date:
2014-12-09
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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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