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Interpretable optimisation-based approach for hyper-box classification.

Georgios I Liapis1, Sophia Tsoka2, Lazaros G Papageorgiou1

  • 1The Sargent Centre for Process Systems Engineering, Department of Chemical Engineering, UCL (University College London), Torrington Place, London, WC1E 7JE UK.

Machine Learning
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Summary
This summary is machine-generated.

This study introduces an optimization-based approach for data classification using hyper-box representations. The method enhances machine learning interpretability by generating compact IF-THEN rules with minimized length and number.

Keywords:
Data classificationHyper-boxInterpretable machine learningMathematical programmingMixed integer optimisation

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Optimization

Background:

  • Data classification is a core machine learning problem.
  • Improving algorithm accuracy and interpretability is crucial.
  • Interpretable models are needed to understand black-box decisions.

Purpose of the Study:

  • To propose an optimization-based approach for multi-class data classification.
  • To utilize hyper-box representations for accurate and interpretable predictions.
  • To facilitate the extraction of compact IF-THEN rules.

Main Methods:

  • Formulating hyper-box classifier training as a Mixed Integer Linear Programming (MILP) model.
  • Employing an optimization-based strategy for rule generation.
  • Minimizing the number and length of IF-THEN rules.

Main Results:

  • The proposed algorithm demonstrates favorable prediction accuracy on real-world datasets.
  • It achieves enhanced interpretability through simplified rule sets.
  • Performance is competitive with well-known alternative algorithms.

Conclusions:

  • The optimization-based hyper-box classifier offers a balance of accuracy and interpretability.
  • The approach effectively extracts compact and simple IF-THEN rules.
  • This method advances interpretable machine learning for data classification.