Learning with multiple Gaussian distance kernels for time series classification

Lei Liu, Wangmeng Zuo, David Zhang, Dongyu Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Various distance measures have been proposed for time series classification, and several of them have been used to construct Gaussian distance kernels for support vector machine (SVM) - based classification. Considering that different Gaussian distance kernels may carry complementary information for classification, in this paper, we propose a multiple kernel learning (MKL) method to integrate multiple Gaussian distance kernels to further improve time series classification accuracy. We first adopt the classical Gaussian RBF (GRBF) kernel and the recently developed Gaussian elastic metric distance kernel (i.e. GERP kernel and GTWED kernel), and then use an efficient MKL, SimpleMKL, to learn the kernel classifier. Our experimental results on 12 UCR time series data sets show that the proposed method is superior to SVM with individual Gaussian distance kernel.

Original languageEnglish
Title of host publication2011 3rd International Conference on Advanced Computer Control, ICACC 2011
Pages624-628
Number of pages5
DOIs
Publication statusPublished - 2011
Event3rd IEEE International Conference on Advanced Computer Control, ICACC 2011 - Harbin, China
Duration: 18 Jan 201120 Jan 2011

Publication series

Name2011 3rd International Conference on Advanced Computer Control, ICACC 2011

Conference

Conference3rd IEEE International Conference on Advanced Computer Control, ICACC 2011
Country/TerritoryChina
CityHarbin
Period18/01/1120/01/11

Keywords

  • kernel method
  • multiple kernel learning
  • support vector machine
  • time series classification

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