Decision forest for multivariate time series analysis

Nine He, Leyang Li, Osamu Yoshie

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルiiWAS2011 - 13th International Conference on Information Integration and Web-Based Applications and Services
ページ60-65
ページ数6
DOI
出版ステータスPublished - 2011 12 1
イベント13th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2011 - Ho Chi Minh City, Viet Nam
継続期間: 2011 12 52011 12 7

出版物シリーズ

名前ACM International Conference Proceeding Series

Conference

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

ASJC Scopus subject areas

  • ソフトウェア
  • 人間とコンピュータの相互作用
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ ネットワークおよび通信

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