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A new dual-scale nearest neighbor statistical feature construction algorithm for imbalanced data oriented to Gaussian

Wei Wang¹, Shuang Ouyang², Fen Liu¹

¹Business School, Guilin Tourism University, Guilin, 541006, China.

Scientific Reports

|June 13, 2026

Summary

This study introduces a new feature construction algorithm, dynamic dual-scale nearest neighbor statistical ratio (NNDSR), to improve Gaussian Naive Bayes (GNB) performance on imbalanced datasets. NNDSR enhances minority class recognition and class separability, outperforming existing methods.

Keywords:

Dynamic dual-scale nearest neighbor statistical ratio Feature construction algorithm Gaussian Naive Bayes classifier Imbalanced datasets

Related Experiment Videos

Area of Science:

Machine Learning
Data Mining
Pattern Recognition

Background:

Gaussian Naive Bayes (GNB) classifier performance degrades on imbalanced datasets.
Sparse minority class features and severe class overlap contribute to GNB performance issues.
Existing methods like sampling and feature enhancement often mismatch GNB's core assumptions.

Purpose of the Study:

Propose a novel feature construction algorithm, dynamic dual-scale nearest neighbor statistical ratio (NNDSR), to address GNB performance degradation on imbalanced data.
Enhance the discriminability and Gaussian distribution adaptability of features.
Improve the recognition accuracy of minority classes and overall classification performance.

Main Methods:

Developed a dynamic dual-scale nearest neighbor mechanism to extract local aggregation and inter-class boundary information.
Generated new features using cross-class and dual-scale statistical ratio operations.
Validated the NNDSR algorithm on 22 UCI datasets with varying characteristics.

Main Results:

NNDSR significantly outperformed the original data and 16 mainstream algorithms across key metrics (AUC, G-mean, F-measure).
Demonstrated notable improvement in minority class recognition accuracy.
Confirmed efficiency and stability through scalability tests on large datasets.

Conclusions:

NNDSR is a robust feature construction algorithm for GNB to effectively handle imbalanced data.
The proposed method avoids information distortion issues of traditional sampling techniques.
NNDSR offers strong practical application value for imbalanced classification tasks.