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Finding rule groups to classify high dimensional gene expression datasets

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conference contribution
posted on 2006-01-01, 00:00 authored by Jiyuan An, Yi-Ping Phoebe Chen
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

History

Event

International Conference on Pattern Recognition (18th : 2006 : Hong Kong)

Pagination

1196 - 1199

Publisher

IEEE Xplore

Location

Hong Kong

Place of publication

Piscataway, N.J.

Start date

2006-08-20

End date

2006-08-24

ISBN-13

9780769525211

ISBN-10

0769525210

Language

eng

Notes

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Publication classification

E1 Full written paper - refereed; 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.

Editor/Contributor(s)

Y Tang, P Wang, G Lorette, D Yeung

Title of proceedings

18th International Conference on Pattern Recognition : proceedings : 20 - 24 August, 2006, Hong Kong

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