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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifiers.

Patryk Orzechowski1,2, Jason H Moore3

  • 1Institute for Biomedical Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104, USA.

Science Advances
|November 23, 2022
PubMed
Summary

We introduce the Diverse and Generative ML Benchmark (DIGEN), a synthetic dataset suite for evaluating machine learning (ML) algorithms. DIGEN aids in understanding ML algorithm performance and identifying areas for improvement in binary classification tasks.

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Area of Science:

  • Computer Science
  • Machine Learning
  • Data Science

Background:

  • Evaluating machine learning (ML) algorithms requires understanding their strengths and weaknesses.
  • Determining the scope of application for ML models is essential for effective implementation.
  • Current benchmarking methods may lack comprehensiveness and interpretability.

Purpose of the Study:

  • Introduce the Diverse and Generative ML Benchmark (DIGEN) for evaluating ML algorithms.
  • Provide a reproducible and interpretable resource for benchmarking ML classification.
  • Facilitate a deeper understanding of ML algorithm performance on binary outcomes.

Main Methods:

  • Developed DIGEN, a resource comprising 40 synthetic datasets generated from mathematical functions.
  • Functions map continuous features to binary targets, creating diverse classification problems.
  • Employed a heuristic algorithm to optimize dataset diversity for varied ML algorithm performance.

Main Results:

  • DIGEN offers a comprehensive test suite for evaluating and comparing ML algorithms.
  • The benchmark maximizes performance diversity across popular ML algorithms.
  • Provides generative functions for interpretable analysis of algorithm weaknesses.

Conclusions:

  • DIGEN enables thorough, reproducible, and interpretable benchmarking of ML algorithms.
  • Facilitates understanding of why certain ML methods underperform.
  • Offers insights for improving ML algorithm development and application in binary classification.