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Author: search.filters.author.Loo, Chu KiongSubject: search.filters.subject.Meta-cognitive learning

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    Real-time financial data prediction using meta-cognitive recurrent kernel online sequential extreme learning machine
    (2019-01-01)
    Liu, Zongying
    ;
    Loo, Chu Kiong
    ;
    Pasupa, Kitsuchart
    This paper proposes a novel algorithm called Meta-cognitive Recurrent Kernel Online Sequential Extreme Learning Machine with a kernel filter and a modified Drift Detector Mechanism (Meta-RKOS-ELM<inf>ALD</inf>-DDM). The algorithm aims to tackle a well-known concept drift problem in time series prediction by utilising the modified concept drift detector mechanism. Moreover, the new meta-cognitive learning strategy is employed to solve parameter dependency and reduce learning time. The experimental results show that the proposed method can achieve better performance than the conventional algorithm in a set of financial datasets.
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    Handling concept drift in time-series data: Meta-cognitive recurrent recursive-kernel OS-ELM
    (2018-01-01)
    Liu, Zongying
    ;
    Loo, Chu Kiong
    ;
    Pasupa, Kitsuchart
    This paper proposes a meta-cognitive recurrent multi-step-prediction model called Meta-cognitive Recurrent Recursive Kernel Online Sequential Extreme Learning Machine with a new modified Drift Detector Mechanism (Meta-RRKOS-ELM-DDM). This model combines the strengths of Recurrent Kernel Online Sequential Extreme Learning Machine (RKOS-ELM) with the recursive kernel method and a new meta-cognitive learning strategy. We apply Drift Detector Mechanism to solve concept drift problem. Recursive kernel method successfully replaces the normal kernel method in RKOS-ELM and generates a fixed reservoir with optimised information. The new meta-cognitive learning strategy can reduce the computational complexity. The experimental results show that Meta-RRKOS-ELM-DDM has a superior prediction ability in different predicting horizons than the others.