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Finding rule groups to classify high dimensional gene expression datasets

An, Jiyuan and Chen, Yi-Ping Phoebe 2006, Finding rule groups to classify high dimensional gene expression datasets, in 18th International Conference on Pattern Recognition : proceedings : 20 - 24 August, 2006, Hong Kong, IEEE Xplore, Piscataway, N.J., pp. 1196-1199.

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Title Finding rule groups to classify high dimensional gene expression datasets
Author(s) An, Jiyuan
Chen, Yi-Ping Phoebe
Conference name International Conference on Pattern Recognition (18th : 2006 : Hong Kong)
Conference location Hong Kong
Conference dates 20-24 August 2006
Title of proceedings 18th International Conference on Pattern Recognition : proceedings : 20 - 24 August, 2006, Hong Kong
Editor(s) Tang, Yuan Yan
Wang, Patrick
Lorette, G.
Yeung, Daniel So
Publication date 2006
Conference series International Conference on Pattern Recognition
Start page 1196
End page 1199
Publisher IEEE Xplore
Place of publication Piscataway, N.J.
Summary Microarray data provides quantitative information about the transcription profile of cells. To analyze microarray datasets, methodology of machine learning has increasingly attracted bioinformatics researchers. Some approaches of machine learning are widely used to classify and mine biological datasets. However, many gene expression datasets are extremely high dimensionality, traditional machine learning methods can not be applied effectively and efficiently. This paper proposes a robust algorithm to find out rule groups to classify gene expression datasets. Unlike the most classification algorithms, which select dimensions (genes) heuristically to form rules groups to identify classes such as cancerous and normal tissues, our algorithm guarantees finding out best-k dimensions (genes), which are most discriminative to classify samples in different classes, to form rule groups for the classification of expression datasets. Our experiments show that the rule groups obtained by our algorithm have higher accuracy than that of other classification approaches
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 0769525210
9780769525211
Language eng
Field of Research 080610 Information Systems Organisation
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30005976

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