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Roza: a new and comprehensive metric for evaluating classification systems.

Mesut Melek1, Negin Melek2

  • 1Department of Electronics and Automation, Gumushane University, Gumushane, Turkey.

Computer Methods in Biomechanics and Biomedical Engineering
|October 25, 2021
PubMed
Summary

A new metric, Roza, offers a comprehensive and fair evaluation for classification systems, overcoming limitations of traditional metrics like accuracy rate (ACC) and area under curve (AUC), especially with imbalanced data.

Keywords:
ClassificationRozacomparisonimbalanced datameasure performance

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Area of Science:

  • Computer Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Traditional metrics like accuracy rate (ACC), area under curve (AUC), Jaccard index (JI), and Cohen's kappa coefficient have limitations in evaluating classification systems.
  • These metrics are insufficient for comparing system superiority, especially with imbalanced datasets.

Purpose of the Study:

  • Introduce the Roza metric for a comprehensive, fair, and accurate evaluation of classification systems.
  • Provide a single-value summary of multiple performance metrics to facilitate system comparison.

Main Methods:

  • Developed the Roza metric, inspired by a polygon representation of superimposed performance metrics.
  • Calculated Roza metric for systems tested under identical conditions across three datasets and strategies.
  • Verified the stability and validity of the Roza metric through comparative analysis.

Main Results:

  • The Roza metric effectively summarizes system performance into a single, understandable value.
  • Demonstrated the Roza metric's capability to provide a comprehensive, fair, and accurate comparison between different classification systems.
  • Confirmed the metric's applicability across diverse classification tasks.

Conclusions:

  • The Roza metric is a powerful tool for evaluating classification systems, particularly excelling where traditional metrics fall short.
  • Roza facilitates robust system comparisons, especially crucial in machine learning and deep learning applications with imbalanced data.
  • The metric's versatility makes it suitable for any system involving classification processes.