# Comparing Two Categorical Gini Correlations with Applications to Classification Problems

arXiv: https://arxiv.org/abs/2605.17763 · HTML: https://mlsanity.com/paper/2605.17763/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 18 May 2026.

## Builds on (most cited first, 8 of 8)

- [Estimating Feature-Label Dependence Using Gini Distance Statistics](https://mlsanity.com/paper/1906.02171/) (2019)
- [A new Gini correlation between quantitative and qualitative variables](https://mlsanity.com/paper/1809.09793/) (2018)
- [Grouped feature screening for ultrahigh-dimensional classification via Gini distance correlation](https://mlsanity.com/paper/2304.08605/) (2023)
- [Jackknife empirical likelihood confidence intervals for the categorical Gini correlation](https://mlsanity.com/paper/2310.09646/) (2023)
- [CopulaSMOTE: A Copula-Based Oversampling Approach for Imbalanced Classification in Diabetes Prediction](https://mlsanity.com/paper/2506.17326/) (2025)
- [gcor: A Python Implementation of Categorical Gini Correlation and Its Inference](https://mlsanity.com/paper/2506.19230/) (2025)
- [Copula Based Fusion of Clinical and Genomic Machine Learning Risk Scores for Breast Cancer Risk Stratification](https://mlsanity.com/paper/2511.17605/) (2025)
- [Bayesian Inference for Joint Tail Risk in Paired Biomarkers via Archimedean Copulas with Restricted Jeffreys Priors](https://mlsanity.com/paper/2602.15319/) (2026)

## Abstract (arXiv)

This article proposes an inferential framework for comparing predictor importance in classification problems with categorical response variables. The approach is based on the categorical Gini correlation (CGC) proposed by Dang et al. (2020), a measure of dependence between numerical predictors and categorical outcomes. Predictor importance is evaluated by testing differences in CGCs across competing predictor groups. The proposed methodology accommodates predictors of arbitrary and unequal dimensions and allows for dependence between predictor groups. Asymptotic normality of the test statistic is established under both the null and alternative hypotheses, and the resulting test is shown to be consistent. In addition to deriving the asymptotic distribution, a nonparametric bootstrap procedure is developed as an alternative approach to inference. Simulation studies, along with applications to breast cancer and human activity recognition datasets, demonstrate the effectiveness of the proposed framework.