Decision forest for multivariate time series analysis

Nine He, Leyang Li, Osamu Yoshie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Nowadays with time series accounting for an increasingly large fraction of world's supply of data, there has been an explosion of interest in mining time series data. This paper proposes a multivariate time series classification model which is both effective in classifier's accuracy and comprehensibility. It is composed of two stages: a supervised clustering for pattern extraction and soft discretization decision forest. In supervised clustering, some real time series instances from the training dataset will be selected as class dedicated patterns. While in decision forest, the rule induction helps to improve the knowledge acquisition of the classifier. In addition, soft discretization would further improve the accuracy and comprehensibility of the classifier.

Original languageEnglish
Title of host publicationiiWAS2011 - 13th International Conference on Information Integration and Web-Based Applications and Services
Pages60-65
Number of pages6
DOIs
Publication statusPublished - 2011 Dec 1
Event13th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2011 - Ho Chi Minh City, Viet Nam
Duration: 2011 Dec 52011 Dec 7

Publication series

NameACM International Conference Proceeding Series

Conference

Conference13th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2011
CountryViet Nam
CityHo Chi Minh City
Period11/12/511/12/7

Keywords

  • decision forest
  • fuzzy partitioning
  • multivariate time series classification
  • soft discretization
  • supervised clustering

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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