Finding rule groups to classify high dimensional gene expression datasets

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 Open Access Collection

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