Recurrent Classifier based on An Incremental Meta-Cognitive-based Scaffolding Algorithm

This paper outlines our proposal for a novel meta-cognitive-based scaffolding classifier, namely Recurrent Classifier (rClass). rClass is capable of emulating three fundamental pillars of human learning in terms of what-to-learn, how-to-learn, and when-to-learn. The cognitive constituent of rClass is underpinned by a recurrent network based on a generalized version of the Takagi Sugeno Kang (TSK) fuzzy system possessing a local feedback of the rule layer. The main basis of the what-to-learn component relies on the new active learning-based conflict measure. Meanwhile, the when-to-learn learning scenario makes use of the standard sample reserved strategy.

The how-to-learn module actualizes the Schema and Scaffolding concepts of cognitive psychology. All learning principles are committed in the single-pass, local learning modes and create a plug-and-play learning foundation minimizing additional pre-or post-training phases. The efficacy of rClass has been scrutinized by means of rigorous empirical studies, statistical tests and benchmarks with state-of-the-art classifiers, which demonstrate the rClass potency in producing reliable classification rates, while retaining low complexity in terms of the rule base burden, computational load and annotation effort.

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