Multivariable stream data classification using motifs and their temporal relations

Sungbo Seo, Jaewoo Kang, Keun Ho Ryu

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Multivariable stream data is becoming increasingly common as diverse types of sensor devices and networks are deployed. Building accurate classification models for such data has attracted a lot of attention from the research community. Most of the previous works, however, relied on features extracted from individual streams, and did not take into account the dependency relations among the features within and across the streams. In this work, we propose new classification models that exploit temporal relations among features. We showed that consideration of such dependencies does significantly improve the classification accuracy. Another benefit of employing temporal relations is the improved interpretability of the resulting classification models, as the set of temporal relations can be easily translated to a rule using a sequence of inter-dependent events characterizing the class. We evaluated the proposed scheme using different classification models including the Naive Bayesian, TFIDF, and vector distance models. We showed that the proposed model can be a useful addition to the set of existing stream classification algorithms.

Original languageEnglish
Pages (from-to)3489-3504
Number of pages16
JournalInformation Sciences
Volume179
Issue number20
DOIs
Publication statusPublished - 2009 Sep 29

Keywords

  • Data classification
  • Motifs
  • Multivariable stream
  • Stream data mining
  • Stream data modeling
  • Temporal relations

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

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