TY - JOUR
T1 - Multivariable stream data classification using motifs and their temporal relations
AU - Seo, Sungbo
AU - Kang, Jaewoo
AU - Ho Ryu, Keun
N1 - Funding Information:
The major part of the work done by Seo and Kang was conducted at Kang’s Lab, North Carolina State University, while they were with the NCSU. This work was partially supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD) (KRF-2007-359-D00015), the Korea Science and Engineering Foundation (KOSEF) Grant funded by the Korean government (MEST) (R01-2008-000-20564-0, R01-2007-000-10926-0, and R11-2008-014-02002-0), the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2009-0077688), the Second Brain Korea 21 Project Grant, Grant (#07KLSGC02) from the Cutting-edge Urban Development – Korean Land Spatialization Research Project funded by the Ministry of Construction & Transportation of the Korean government, and the Korea Research Foundation Grant funded by the Korean Government(MEST, The Regional Core Research Program/Chungbuk BIT Research-Oriented University Consortium).
PY - 2009/9/29
Y1 - 2009/9/29
N2 - 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.
AB - 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.
KW - Data classification
KW - Motifs
KW - Multivariable stream
KW - Stream data mining
KW - Stream data modeling
KW - Temporal relations
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U2 - 10.1016/j.ins.2009.06.036
DO - 10.1016/j.ins.2009.06.036
M3 - Article
AN - SCOPUS:68049100107
VL - 179
SP - 3489
EP - 3504
JO - Information Sciences
JF - Information Sciences
SN - 0020-0255
IS - 20
ER -