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Updated: Oct 10, 2025

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High-Dimensional Unbalanced Binary Classification by Genetic Programming with Multi-Criterion Fitness Evaluation and

Wenbin Pei1, Bing Xue2, Lin Shang3

  • 1School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington 6140, New Zealand Wenbin.Pei@ecs.vuw.ac.nz.

Evolutionary Computation
|December 13, 2021
PubMed
Summary

This study introduces a new genetic programming (GP) method to improve high-dimensional unbalanced classification. The novel approach uses a two-criterion fitness function and tournament selection to reduce bias and enhance minority class accuracy.

Keywords:
Classificationclass imbalancegenetic programminghigh dimensionality

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Area of Science:

  • Machine Learning
  • Computational Intelligence
  • Bioinformatics

Background:

  • High-dimensional unbalanced classification presents significant challenges due to the combined effects of numerous features and skewed class distributions.
  • Genetic programming (GP) offers feature selection capabilities beneficial for high-dimensional data but struggles with class imbalance, leading to biased models favoring the majority class.
  • Accurate classification of the minority class is often critical in many applications, necessitating methods that address GP's inherent bias.

Purpose of the Study:

  • To develop an effective genetic programming (GP) approach for high-dimensional unbalanced classification.
  • To mitigate the performance bias of GP classifiers on imbalanced datasets.
  • To improve the accurate classification of minority class instances in high-dimensional settings.

Main Methods:

  • A novel two-criterion fitness function was developed, integrating the approximation of the area under the curve (AUC) and classification clarity.
  • The two criteria of the fitness function were combined in pairs rather than through summation.
  • A three-criterion tournament selection mechanism was designed to effectively identify and select superior programs for evolutionary advancement.

Main Results:

  • The proposed GP method demonstrated superior classification performance compared to existing methods.
  • The novel fitness function and selection strategy effectively addressed the performance bias issue in GP for unbalanced datasets.
  • Experimental results confirmed improved accuracy, particularly for the minority class.

Conclusions:

  • The developed GP approach offers a robust solution for high-dimensional unbalanced classification tasks.
  • Combining AUC approximation and classification clarity in a paired fitness function significantly enhances classifier performance.
  • The three-criterion tournament selection further optimizes the evolutionary process, leading to more effective and less biased models.