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Meta-learning for data summarization based on instance selection method

Smith-Miles, Kate and Islam, Rafiqul 2010, Meta-learning for data summarization based on instance selection method, in WCCI 2010 : IEEE World Congress on Computational Intelligence, IEEE, Piscataway, N.J., pp. 1-8.

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Title Meta-learning for data summarization based on instance selection method
Author(s) Smith-Miles, Kate
Islam, Rafiqul
Conference name IEEE World Congress on Computational Intelligence (2010 : Barcelona, Spain)
Conference location Barcelona, Spain
Conference dates 18-23 Jul. 2010
Title of proceedings WCCI 2010 : IEEE World Congress on Computational Intelligence
Editor(s) [Unknown]
Publication date 2010
Conference series IEEE World Congress on Computational Intelligence
Start page 1
End page 8
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Summary The purpose of instance selection is to identify which instances (examples, patterns) in a large dataset should be selected as representatives of the entire dataset, without significant loss of information. When a machine learning method is applied to the reduced dataset, the accuracy of the model should not be significantly worse than if the same method were applied to the entire dataset. The reducibility of any dataset, and hence the success of instance selection methods, surely depends on the characteristics of the dataset, as well as the machine learning method. This paper adopts a meta-learning approach, via an empirical study of 112 classification datasets from the UCI Repository [1], to explore the relationship between data characteristics, machine learning methods, and the success of instance selection method.
ISBN 9781424481262
9781424469109
Language eng
Field of Research 080303 Computer System Security
Socio Economic Objective 890206 Internet Hosting Services (incl. Application Hosting Services)
HERDC Research category E1 Full written paper - refereed
HERDC collection year 2010
Copyright notice ©2010, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30034406

Document type: Conference Paper
Collection: School of Information Technology
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