Multivariable stream data classification using motifs and their temporal relations
- Authors
- Seo, Sungbo; Kang, Jaewoo; Ryu, Keun Ho
- Issue Date
- 29-Sep-2009
- Publisher
- ELSEVIER SCIENCE INC
- Keywords
- Stream data mining; Data classification; Motifs; Temporal relations; Multivariable stream; Stream data modeling
- Citation
- INFORMATION SCIENCES, v.179, no.20, pp.3489 - 3504
- Indexed
- SCIE
SCOPUS
- Journal Title
- INFORMATION SCIENCES
- Volume
- 179
- Number
- 20
- Start Page
- 3489
- End Page
- 3504
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/119293
- DOI
- 10.1016/j.ins.2009.06.036
- ISSN
- 0020-0255
- 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. (C) 2009 Elsevier Inc. All rights reserved.
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Collections - Graduate School > Department of Computer Science and Engineering > 1. Journal Articles
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