Utility of real-time decision-making in commercial data stream mining domains

Phua, Clifton, Lee, Vincent C. S. and Smith-Miles, Kate 2008, Utility of real-time decision-making in commercial data stream mining domains, in ICSSSM 2008 : Exploring service dynamics with science and innovative technology : Proceedings of the 2008 International Conference on Service Systems and Service Management, IEEE, Piscataway, N.J., pp. 1-6.

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Title Utility of real-time decision-making in commercial data stream mining domains
Author(s) Phua, Clifton
Lee, Vincent C. S.
Smith-Miles, Kate
Conference name IEEE International Conference on Service Systems and Service Management (2008 : Melbourne, Vic.)
Conference location Melbourne, Vic.
Conference dates 30 June - 2 July 2008
Title of proceedings ICSSSM 2008 : Exploring service dynamics with science and innovative technology : Proceedings of the 2008 International Conference on Service Systems and Service Management
Editor(s) Lee, Vincent C. S.
Chen, Jian
Ng, Wee-Keong
Ong, Kok-Leong
Tan, Ting Yean
Publication date 2008
Conference series International Conference on Service Systems and Service Management
Start page 1
End page 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) costs and benefits
measurement
real-time decision-making
utility
Summary The objective is to measure utility of real-time commercial decision making. It is important due to a higher possibility of mistakes in real-time decisions, problems with recording actual occurrences, and significant costs associated with predictions produced by algorithms. The first contribution is to use overall utility and represent individual utility with a monetary value instead of a prediction. The second is to calculate the benefit from predictions using the utility-based decision threshold. The third is to incorporate cost of predictions. For experiments, overall utility is used to evaluate communal and spike detection, and their adaptive versions. The overall utility results show that with fewer alerts, communal detection is better than spike detection. With more alerts, adaptive communal and spike detection are better than their static versions. To maximise overall utility with all algorithms, only 1% to 4% in the highest predictions should be alerts.
ISBN 9781424416721
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2008, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30018144

Document type: Conference Paper
Collection: School of Engineering and Information Technology
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