A Novel Meta-Cognitive Extreme Learning Machine to Learning from Data Streams

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2015, pp. 2792 - 2797
Issue Date:
2015
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Extreme Learning Machine (ELM) is an answer to an increasing demand for a low-cost learning algorithm to handle big data applications. Nevertheless, existing ELMs leave four uncharted problems: complexity, uncertainty, concept drifts, curse of dimensionality. To correct these issues, a novel incremental meta-cognitive ELM, namely Evolving Type-2 Extreme Learning Machine (eT2ELM), is proposed. Et2Elm is built upon the three pillars of meta-cognitive learning, namely what-to-learn, how-to-learn, when-to-learn, where the notion of ELM is implemented in the how-to-learn component. On the other hand, eT2ELM is driven by a generalized interval type-2 Fuzzy Neural Network (FNN) as the cognitive constituent, where the interval type-2 multivariate Gaussian function is used in the hidden layer, whereas the nonlinear Chebyshev function is embedded in the output layer. The efficacy of eT2ELM is proven with four data streams possessing various concept drifts, comparisons with prominent classifiers, and statistical tests, where eT2ELM demonstrates the most encouraging learning performances in terms of accuracy and complexity.
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