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Cost-sensitive Performance Metric for Comparing Multiple Ordinal Classifiers.

Nysia I George1, Tzu-Pin Lu2, Ching-Wei Chang1

  • 1Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, FDA, Jefferson, AR 72079, USA.

Artificial Intelligence Research
|January 11, 2017
PubMed
Summary
This summary is machine-generated.

A new cost-sensitive metric balances predictive accuracy and misclassification costs for ordinal classification in biomedical science. This approach improves identifying optimal ordinal classifiers, crucial for personalized medicine applications.

Keywords:
ClassificationCost-sensitiveMisclassificationOrdinal classificationOrdinal dataPerformance metric

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Area of Science:

  • Biomedical Science
  • Machine Learning
  • Ordinal Classification

Background:

  • Personalized and precision medicine drive demand for ordinal classification in biomedical science.
  • Current metrics (accuracy, Kendall's τ b, MAE) lack a unified view of accuracy and misclassification costs.
  • Pairwise analysis of existing metrics yields inconsistent findings.

Purpose of the Study:

  • To introduce a novel cost-sensitive metric for ordinal classification.
  • To optimize the tradeoff between predictive accuracy and misclassification cost.
  • To develop a comprehensive tool for comparing ordinal classifiers.

Main Methods:

  • A new cost-sensitive metric was developed.
  • The metric accounts for ordinal data structure, total misclassification cost, and imbalanced classes.
  • Evaluated using three real cancer datasets and four simulation studies.

Main Results:

  • The proposed cost-sensitive metric demonstrated superior performance in identifying optimal ordinal classifiers.
  • The metric effectively balances accuracy with misclassification costs.
  • Provides a robust tool for comparative analysis of competing ordinal classifiers.

Conclusions:

  • The new metric offers a comprehensive approach to ordinal classification evaluation in biomedical applications.
  • Integrating misclassification costs is imperative for real-world ordinal classification decisions.
  • This work paves the way for incorporating cost-sensitivity into ordinal prediction modeling algorithms.