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.
(Some files may be inaccessible until you login with your Deakin Research Online credentials)
18th International Conference on Pattern Recognition : proceedings : 20 - 24 August, 2006, Hong Kong
Tang, Yuan Yan Wang, Patrick Lorette, G. Yeung, Daniel So
International Conference on Pattern Recognition
Place of publication
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
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.
Unless expressly stated otherwise, the copyright for items in Deakin Research Online is owned by the author, with all rights reserved.
Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO.
If you believe that your rights have been infringed by this repository, please contact firstname.lastname@example.org.