Recurrent Classifier Based on an Incremental Metacognitive-Based Scaffolding Algorithm

• Published in: IEEE transactions on fuzzy systems 2015-12, Vol.23 (6), p.2048-2066
• Main Authors: Pratama, Mahardhika, Anavatti, Sreenatha G., Jie Lu
• Format: Article
• Language: English
• Subjects: Chebyshev approximation; Complexity theory; Covariance matrices; Delamination; Evolving Fuzzy System; Evolving Neuro-Fuzzy System; Merging; meta-cognitive learning; Online Learning; Training; Vectors
• ISSN: 1063-6706; 1941-0034
• DOI: 10.1109/TFUZZ.2015.2402683

This paper outlines our proposal for a novel metacognitive-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 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.