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Handling Concept Drift in Time-Series Data: Meta-cognitive Recurrent Recursive-Kernel OS-ELM

  • Zongying Liu
  • Chu Kiong Loo
  • Kitsuchart PasupaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)

Abstract

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.

Keywords

Time series Recursive kernel Recurrent Kernel Adaptive Filter Concept drift Meta-cognitive learning 

Notes

Acknowledgement

The authors would like to express special thanks of gratitude to UM Grand Challenge from the University of Malaya under Grant GC003A-14HTM, FRGS grant from MOHE FP069-2015A, and the Thailand Research Fund under grant agreement No. TRG5680090 which support our research.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Zongying Liu
    • 1
  • Chu Kiong Loo
    • 1
  • Kitsuchart Pasupa
    • 2
    Email author
  1. 1.Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  2. 2.Faculty of Information TechnologyKing Mongkut’s Institute of Technology LadkrabangBangkokThailand

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