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Semisupervised Ordinal Regression Based on Empirical Risk Minimization.

Taira Tsuchiya1, Nontawat Charoenphakdee2, Issei Sato3

  • 1University of Tokyo, Bunkyo-ku, Tokyo, 113-0333, Japan, and RIKEN AIP: Chuo-ku, Tokyo 103-0027, Japan tsuchiya@sys.i.kyoto-u.ac.jp.

Neural Computation
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Summary
This summary is machine-generated.

This study introduces a new framework for semisupervised ordinal regression, improving prediction accuracy with unlabeled data. The method optimizes various performance metrics without restrictive assumptions, offering greater flexibility for ordinal classification tasks.

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Area of Science:

  • Machine Learning
  • Statistics
  • Computer Science

Background:

  • Ordinal regression predicts ordered categories.
  • Semisupervised learning utilizes both labeled and unlabeled data.
  • Existing methods lack metric optimization and theoretical guarantees.

Purpose of the Study:

  • Propose a novel generic framework for semisupervised ordinal regression.
  • Address limitations of existing methods regarding evaluation metrics and model flexibility.
  • Provide theoretical guarantees for the proposed approach.

Main Methods:

  • Utilize the empirical risk minimization principle.
  • Develop a framework applicable to various performance metrics (MAE, zero-one, MSE).
  • Incorporate flexible choices for models, surrogate losses, and optimization algorithms.

Main Results:

  • The framework optimizes multiple evaluation metrics for ordinal regression.
  • No geometric assumptions on unlabeled data are required.
  • An estimation error bound demonstrates the consistency of the risk estimator.

Conclusions:

  • The proposed framework offers a flexible and theoretically grounded approach to semisupervised ordinal regression.
  • It enhances the performance of ordinal classification by effectively leveraging unlabeled data.
  • Experimental results validate the framework's usefulness and broad applicability.