Scaffolding Type-2 Classifier for Incremental Learning under Concept Drifts
Sprache des Titels:
Englisch
Original Kurzfassung:
The proposal of a meta-cognitive learning machine that embodies the three pillars of human
learning: what-to-learn, how-to-learn, and when-to-learn, has enriched the landscape of evolving systems. The
majority of meta-cognitive learning machines in the literature have not, however, characterised a plug-and-play
working principle, and thus require supplementary learning modules to be pre-or post-processed. In addition,
they still rely on the type-1 neuron, which has problems of uncertainty. This paper proposes the Scaffolding
Type-2 Classifier (ST2Class). ST2Class is a novel meta-cognitive scaffolding classifier that operates completely
in local and incremental learning modes. It is built upon a multivariable interval type-2 Fuzzy Neural Network
(FNN) which is driven by multivariate Gaussian function in the hidden layer and the non-linear wavelet
polynomial in the output layer. The what-to-learn module is created by virtue of a novel active learning scenario
termed the uncertainty measure; the how-to-learn module is based on the renowned Schema and Scaffolding
theories; and the when-to-learn module uses a standard sample reserved strategy. The viability of ST2Class is
numerically benchmarked against state-of-the-art classifiers in 12 data streams, and is statistically validated by
thorough statistical tests, in which it achieves high accuracy while retaining low complexity.
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