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Fisher's Exact Test

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Related Experiment Videos

Representation of a Fisher criterion function in a kernel feature space.

Sang Wan Lee1, Zeungnam Bien

  • 1Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea. bigbean0@gmail.com

IEEE Transactions on Neural Networks
|December 17, 2009
PubMed
Summary

This study introduces a new method for kernel classification, optimizing kernel function parameters for better separability. The approach simplifies Fisher criterion computation using kernel matrices, improving classification accuracy in applications like prostate cancer detection.

Related Experiment Videos

Area of Science:

  • Machine Learning
  • Computational Biology
  • Statistical Pattern Recognition

Background:

  • Kernel methods are powerful tools for classification tasks.
  • Optimizing kernel function parameters is crucial for performance.
  • The Fisher criterion is a key metric for evaluating separability.

Purpose of the Study:

  • To represent the Fisher criterion in kernel feature space.
  • To develop a computationally efficient method for optimizing kernel parameters.
  • To demonstrate the application of the proposed method in cancer classification.

Main Methods:

  • Deriving a kernel feature space representation of the Fisher criterion.
  • Computing Fisher function values using kernel matrix block averages.
  • Identifying the ideal kernel matrix as a global optimum for the Fisher criterion.
  • Relating the ideal kernel matrix to empirical kernel target alignment.

Main Results:

  • The Fisher criterion can be efficiently computed using kernel matrix block averages.
  • The ideal kernel matrix globally maximizes the Fisher criterion.
  • Kernel parameter optimization is readily achievable.
  • The method shows promise in classifying prostate cancer from microarray data.

Conclusions:

  • The proposed method offers an efficient way to optimize kernel parameters for classification.
  • This approach enhances the separability of data in kernel feature spaces.
  • The findings have practical implications for biomedical data analysis and cancer diagnostics.