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

ABSTRACT: As a widely used classification model, the Gaussian Naive Bayes (GNB) classifier experiences a significant decline in performance when handling imbalanced data. Most traditional approaches rely on sampling techniques; however, these methods alter the quantity and distribution of the original data and are prone to issues such as class overlap and overfitting, thus presenting clear limitations. This article proposes a coordinate transformation algorithm based on radial local relative density changes (RLDC). A key feature of this algorithm is that it preserves the original dataset's quantity and distribution. Instead of modifying the data, it enhances classification performance by generating new features that more prominently represent minority classes. The algorithm transforms the dataset fr

SUBMITTER: Wang W 

PROVIDER: S-EPMC12453872 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature