Title page for etd-0730110-230815


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URN etd-0730110-230815
Author Kuo-yi Wu
Author's Email Address kuohsin68@gmail.com
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Department Computer Science and Engineering
Year 2009
Semester 2
Degree Master
Type of Document
Language English
Title GAGS : A Novel Microarray Gene Selection Algorithm for Gene Expression Classification
Date of Defense 2010-07-01
Page Count 57
Keyword
  • Feature selection
  • Gene expression data analysis
  • Genetic algorithm
  • Abstract 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.
    Advisory Committee
  • Chaur-Chin Chen - chair
  • Cheng-Wen Ko - co-chair
  • Chuan-Wen Chiang - co-chair
  • Kuo-Sheng Cheng - co-chair
  • Chung-Nan Lee - advisor
  • Files
  • etd-0730110-230815.pdf
  • indicate access worldwide
    Date of Submission 2010-07-30

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