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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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  3. Improving Gaussian Naive Bayes Classification On Imbalanced Data Through Coordinate-based Minority Feature Mining.
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  3. Improving Gaussian Naive Bayes Classification On Imbalanced Data Through Coordinate-based Minority Feature Mining.

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Improving Gaussian Naive Bayes classification on imbalanced data through coordinate-based minority feature mining.

Wei Wang1, Li Yan1, Fen Liu1

  • 1School of Business, Guilin Tourism University, Guilin, Guangxi, China.

Peerj. Computer Science
|September 24, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel coordinate transformation algorithm to improve Gaussian Naive Bayes (GNB) classification performance on imbalanced data. The radial local relative density changes (RLDC) method enhances minority class representation without altering data distribution, outperforming traditional sampling techniques.

Keywords:
Coordinate transformationGaussian Naive Bayes classifierImbalanced dataMinority class salient features

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Area of Science:

  • Machine Learning
  • Data Science
  • Artificial Intelligence

Background:

  • Gaussian Naive Bayes (GNB) classifiers struggle with imbalanced datasets, leading to performance degradation.
  • Existing sampling techniques for imbalanced data alter data distribution and can cause overfitting or class overlap.
  • There is a need for methods that improve GNB performance on imbalanced data without modifying the original dataset.

Purpose of the Study:

  • To propose a novel coordinate transformation algorithm based on radial local relative density changes (RLDC).
  • To enhance GNB classification performance on imbalanced datasets by generating new features.
  • To preserve the original data's quantity and distribution while improving minority class representation.

Main Methods:

  • Developed a coordinate transformation algorithm that converts absolute coordinates to RLDC-relative coordinates.
  • The RLDC transformation reveals latent local relative density change features, highlighting minority class patterns.
  • Applied the transformed features to the GNB classifier to improve minority class probability estimation.

    • Main Results:

    • The RLDC-based coordinate transformation algorithm significantly improved GNB performance on 20 imbalanced datasets.
    • The algorithm outperformed 14 traditional sampling algorithms across three classification evaluation metrics.
    • Achieved average performance improvements of 21.84%, 33.45%, and 54.63% compared to existing methods.

    Conclusions:

    • The RLDC coordinate transformation offers a novel and effective approach to handling imbalanced data in GNB classification.
    • This method enhances classification accuracy by creating informative features without altering the original data.
    • The algorithm demonstrates significant theoretical and practical value for imbalanced classification problems.