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Adaptively weighted large-margin angle-based classifiers.

Sheng Fu1,2, Sanguo Zhang1,2, Yufeng Liu3

  • 1School of Mathematical Science, University of the Chinese Academy of Sciences, Beijing 100049, China.

Journal of Multivariate Analysis
|December 15, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces novel angle-based large-margin classifiers that bypass inefficient sum-to-zero constraints. These new methods offer robust and stable multicategory classification, outperforming existing techniques.

Keywords:
Fisher ConsistencyMulticategory ClassificationRobustnessSVMWeighted Learning

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Area of Science:

  • Machine Learning
  • Computer Science
  • Statistical Classification

Background:

  • Large-margin classifiers are effective for binary classification but challenging for multicategory problems.
  • Existing multicategory methods often use inefficient sum-to-zero constraints and are sensitive to outliers.

Purpose of the Study:

  • To develop novel large-margin classification techniques for multicategory problems.
  • To address the limitations of existing methods, including inefficiency and outlier sensitivity.

Main Methods:

  • Utilizing an angle-based classification framework to eliminate the sum-to-zero constraint.
  • Proposing two adaptively weighted large-margin classification techniques.

Main Results:

  • The proposed methods are Fisher consistent.
  • The new techniques demonstrate increased robustness against outliers.
  • Numerical experiments show competitive and stable performance compared to existing approaches.

Conclusions:

  • Angle-based classification offers an efficient alternative for multicategory problems.
  • Adaptively weighted large-margin classifiers provide robust and high-performing solutions.