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Enhancing binary classification: A new stacking method via leveraging computational geometry

Wei Wu, Liang Tang, Zhongjie Zhao, Chung-Piaw Teo

TL;DR

A novel approach that integrates computational geometry techniques, specifically solving the maximum weighted rectangle problem, to develop a new meta-model for binary classification, offering a fresh evaluation perspective.

Abstract

Stacking, a potent ensemble learning method, leverages a meta-model to harness the strengths of multiple base models, thereby enhancing prediction accuracy. Traditional stacking techniques typically utilize established learning models, such as logistic regression, as the meta-model. This paper introduces a novel approach that integrates computational geometry techniques, specifically solving the maximum weighted rectangle problem, to develop a new meta-model for binary classification. Our method is evaluated on multiple open datasets, with statistical analysis showing its stability and demonstrating improvements in accuracy compared to current state-of-the-art stacking methods with out-of-fold predictions. This new stacking method also boasts two significant advantages: enhanced interpretability and the elimination of hyperparameter tuning for the meta-model, thus increasing its practicality. These merits make our method highly applicable not only in stacking ensemble learning but also in various real-world applications, such as hospital health evaluation scoring and bank credit scoring systems, offering a fresh evaluation perspective.

Enhancing binary classification: A new stacking method via leveraging computational geometry

TL;DR

A novel approach that integrates computational geometry techniques, specifically solving the maximum weighted rectangle problem, to develop a new meta-model for binary classification, offering a fresh evaluation perspective.

Abstract

Stacking, a potent ensemble learning method, leverages a meta-model to harness the strengths of multiple base models, thereby enhancing prediction accuracy. Traditional stacking techniques typically utilize established learning models, such as logistic regression, as the meta-model. This paper introduces a novel approach that integrates computational geometry techniques, specifically solving the maximum weighted rectangle problem, to develop a new meta-model for binary classification. Our method is evaluated on multiple open datasets, with statistical analysis showing its stability and demonstrating improvements in accuracy compared to current state-of-the-art stacking methods with out-of-fold predictions. This new stacking method also boasts two significant advantages: enhanced interpretability and the elimination of hyperparameter tuning for the meta-model, thus increasing its practicality. These merits make our method highly applicable not only in stacking ensemble learning but also in various real-world applications, such as hospital health evaluation scoring and bank credit scoring systems, offering a fresh evaluation perspective.

Paper Structure

This paper contains 17 sections, 6 equations, 2 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Binary Classification by Stacking Ensemble Learning
  3. Binary Classification
  4. Stacking Ensemble Learning
  5. Out-of-Fold Predictions
  6. Stacking Ensemble Learning via Computational Geometry
  7. Maximum Weighted Rectangle Problem
  8. Stacking by Solving the MWRP
  9. Metaheuristic Design
  10. Computational Experiments
  11. Datasets
  12. Computational Environment and Implementation Details
  13. Computational Results
  14. Possible Extensions
  15. Interpretable Binary Classifier
  16. ...and 2 more sections

Figures (2)

  • Figure 1: Framework of stacking ensemble learning with $l$ base models.
  • Figure 2: MWRP in meta-model training.