Meta-cognitive recurrent kernel online sequential extreme learning machine with kernel adaptive filter for concept drift handling. (February 2020)

• Record Type: Journal Article
• Authors: Liu, Zongying; Loo, Chu Kiong; Pasupa, Kitsuchart; Seera, Manjeevan
• Is Part Of: Engineering applications of artificial intelligence. Volume 88(2020)
• Publication Date: 2020-02
• Subjects: Concept drift -- Multi-step prediction -- Recurrent algorithm -- Kernel method -- Time series prediction; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.103327
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 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-ELMALD ). 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-ELMALD with DDM has better forecasting ability in various predicting periods with the shortest learning time, as compared with other algorithms.