Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.neucom.2015.06.022 | Neurocomputing |
Keywords | Field | DocType |
Evolving fuzzy systems,Fuzzy neural networks,Meta-cognitive learning,Sequential learning | Algorithmic learning theory,Stability (learning theory),Active learning,Learning theory,Computer science,Artificial intelligence,Computational learning theory,Artificial neural network,Sequence learning,Machine learning,Learning classifier system | Journal |
Volume | Issue | ISSN |
171 | C | 0925-2312 |
Citations | PageRank | References |
41 | 1.17 | 53 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahardhika Pratama | 1 | 702 | 50.02 |
| Jie Lu | 2 | 578 | 38.78 |
| Sreenatha G. Anavatti | 3 | 249 | 39.07 |
| Edwin Lughofer | 4 | 1940 | 99.72 |
| Chee Peng Lim | 5 | 1459 | 122.04 |