Scoring Method for Tumor Prediction from Microarray Data Using an Evolutionary Fuzzy Classifier

  • Shinn-Ying Ho
  • Chih-Hung Hsieh
  • Kuan-Wei Chen
  • Hui-Ling Huang
  • Hung-Ming Chen
  • Shinn-Jang Ho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)


In this paper, we propose a novel scoring method for tumor prediction using an evolutionary fuzzy classifier which can provide accurate and interpretable information. The merits of the proposed method are threefold. 1) The score ranged in [0, 100] can further illustrate the degree of tumor status in contrast to the conventional tumor classifier. 2) The derived score system can be used as a tumor classifier using a system-suggested or human-specified threshold value. 3) The derived classifier with a compact fuzzy rule base can generate an interpretable and accurate prediction result. The effectiveness of the proposed method is evaluated and compared using two well-known datasets from microarray data and an existing tumor classifier. It is shown by computer simulation that the proposed scoring method is effective using ROC curves of classification.


Support Vector Machine Fuzzy Rule Probabilistic Neural Network Tumor Classifier Fuzzy Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shinn-Ying Ho
    • 1
  • Chih-Hung Hsieh
    • 1
  • Kuan-Wei Chen
    • 1
  • Hui-Ling Huang
    • 2
  • Hung-Ming Chen
    • 3
  • Shinn-Jang Ho
    • 4
  1. 1.Institute of BioinformaticsNational Chiao Tung UniversityHsinchuTaiwan
  2. 2.Department of Information ManagementJin Wen Institute of TechnologyHsin-Tien, TaipeiTaiwan
  3. 3.Institute of Information Engineering and Computer ScienceFeng Chia UniversityTaichungTaiwan
  4. 4.Department of Automation EngineeringNational Formosa UniversityHuwei, YunlinTaiwan

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