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GAGS : A Novel Microarray Gene Selection Algorithm for Gene Expression Classification

In this thesis, we have proposed a novel microarray gene selection algorithm consisting of five processes for solving gene expression classification problem. A normalization process is first used to remove the differences among different scales of genes. Second, an efficient gene ranking process is proposed to filter out the unrelated genes. Then, the genetic algorithm is adopted to find the informative gene subsets for each class. For each class, these informative gene subsets are adopted to classify the testing dataset separately. Finally, the separated classification results are fused to one final classification result.
In the first experiment, 4 microarray datasets are used to verify the performance of the proposed algorithm. The experiment is conducted using the leave-one-out-cross-validation (LOOCV) resampling method. We compared the proposed algorithm with twenty one existing methods. The proposed algorithm obtains three wins in four datasets, and the accuracies of three datasets all reach 100%. In the second experiment, 9 microarray datasets are used to verify the proposed algorithm. The experiment is conducted using 50% VS 50% resampling method. Our proposed algorithm obtains eight wins among nine datasets for all competing methods.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0730110-230815
Date30 July 2010
CreatorsWu, Kuo-yi
ContributorsChaur-Chin Chen, Chung-Nan Lee, Chuan-Wen Chiang, Kuo-Sheng Cheng, Cheng-Wen Ko
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730110-230815
Rightsunrestricted, Copyright information available at source archive

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