Decision Boundary Analysis Feature Selection for Breast Cancer Diagnosis.

Abstract

The general pattern recognition problem always involves the extraction of features to be used in pattern classification. There are no theoretical limitations to the number of features which can be obtained for a given pattern recognition problem. This research will develop a correlation procedure for screening a large feature set without the use of a trained classifier. The results will be compared to established saliency metrics such as the Fisher ratio and derivative-based techniques such as Ruck's saliency.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1997
Accession Number
ADA323712

Entities

People

  • Daniel W. Gregg

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Artificial Intelligence
  • Breast Cancer
  • Classification
  • Data Science
  • Databases
  • Dimensionality Reduction
  • Eigenvalues
  • Electrical Engineering
  • Feature Selection
  • Image Processing
  • Information Science
  • Machine Learning
  • Neoplasms
  • Pattern Recognition
  • Recognition
  • Two Dimensional

Readers

  • Computer Vision.

Technology Areas

  • AI & ML