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Author: search.filters.author.Loo, Chu KiongSubject: search.filters.subject.Concept drift

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    Meta-cognitive recurrent kernel online sequential extreme learning machine with kernel adaptive filter for concept drift handling
    (2020-02-01)
    Liu, Zongying
    ;
    Loo, Chu Kiong
    ;
    Pasupa, Kitsuchart
    ;
    Seera, Manjeevan
    This paper proposes a multi-step prediction model for time series prediction, i.e. Meta-cognitive Recurrent Kernel Online Sequential Extreme Learning Machine with Drift Detector Mechanism (Meta-RKOS-ELM<inf>ALD</inf>). Recurrent multi-step algorithm is applied to release the limitation in the number of prediction steps, and Drift Detector Mechanism (DDM) is used to overcome the problem of concept drift in the prediction model. The new meta-cognitive strategy decides the way of the incoming data during training, which decreases the training computation of prediction model and solves the parameter dependency. In our evaluation, we use a total of six artificial data sets and three real-world data sets (Standard & Poor's 500 Index, Shanghai Stock Exchange Composite Index, and Ozone Concentration in Toronto) to prove the ability of kernel filters, the detecting ability of concept drift detector, and situation of applying meta-cognitive strategy in our proposed model. Experiments results indicate that the Meta-KOS-ELM<inf>ALD</inf> with DDM has better forecasting ability in various predicting periods with the shortest learning time, as compared with other algorithms.
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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.