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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Constructing better classifier ensemble based on weighted accuracy and diversity measure.

Xiaodong Zeng1, Derek F Wong1, Lidia S Chao1

  • 1NLP CT Lab/Department of Computer and Information Science, University of Macau, Taipa 999078, Macau.

Thescientificworldjournal
|March 28, 2014
PubMed
Summary
This summary is machine-generated.

A novel Weighted Accuracy and Diversity (WAD) method balances classifier ensemble accuracy and diversity. This approach enhances predictive performance on unseen data, outperforming existing measures in ensemble selection tasks.

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Classifier ensembles aim to improve predictive performance.
  • Balancing accuracy and diversity is crucial for robust ensembles.
  • Existing measures may not effectively optimize both accuracy and diversity.

Purpose of the Study:

  • To introduce a novel Weighted Accuracy and Diversity (WAD) measure.
  • To evaluate the WAD measure's effectiveness in classifier ensemble selection.
  • To find an optimal balance between accuracy and diversity for enhanced predictive ability.

Main Methods:

  • The WAD measure computes the harmonic mean of accuracy and diversity.
  • Weight parameters are utilized to balance accuracy and diversity.
  • The WAD measure was compared against Kappa-Error, GenDiv, and threshold measures using genetic and hill-climbing algorithms on 15 UCI datasets.

Main Results:

  • The WAD measure demonstrated superior performance compared to existing methods in most ensemble selection tasks.
  • The proposed method effectively balances accuracy and diversity.
  • Empirical results confirm the WAD measure's advantage in enhancing predictive ability.

Conclusions:

  • The WAD measure offers a robust approach to evaluating and selecting classifier ensembles.
  • Achieving a balance between accuracy and diversity is key to improving generalization performance.
  • The WAD method provides a valuable tool for optimizing ensemble models.