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Determination of the Mechanical Properties of Flexible Connectors for Use in Insulated Concrete Wall Panels
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Wrapper consistency analysis: a measure of consistency in a wrapper setting.

Chad Kimmel1, James Lyons-Weiler

  • 1Department of Biomedical Informatics, University of Pittsburg, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|November 13, 2008
PubMed
Summary

We introduce Wrapper Consistency Analysis, a new feature selection method for high-throughput data. This approach optimizes predictive accuracy and feature set consistency, offering a more robust evaluation of feature relevance.

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Area of Science:

  • Bioinformatics
  • Statistical Learning
  • Computational Biology

Background:

  • High-throughput data analysis often faces the challenge of numerous features compared to samples.
  • Traditional feature selection primarily relies on predictive accuracy.
  • This can lead to suboptimal feature sets lacking robustness.

Purpose of the Study:

  • To present a novel feature selection method, Wrapper Consistency Analysis.
  • To enhance feature selection by optimizing both predictive accuracy and feature set consistency.
  • To provide a more comprehensive measure of feature set quality.

Main Methods:

  • Developed Wrapper Consistency Analysis, a new feature selection technique.
  • Incorporated a measure of overlap or consistency alongside predictive accuracy.
  • Applied the method to high-throughput datasets.

Main Results:

  • Demonstrated that Wrapper Consistency Analysis optimizes both predictive accuracy and consistency.
  • Showcased the method's ability to identify more robust feature sets.
  • Provided evidence that consistency offers valuable insights beyond accuracy.

Conclusions:

  • Wrapper Consistency Analysis offers an improved approach to feature selection in high-dimensional data.
  • Optimizing for both accuracy and consistency yields more reliable feature subsets.
  • This method enhances the understanding of feature set 'goodness'.