most citedCopulaSMOTE: A Copula-Based Oversampling Approach for Imbalanced Classification in Diabetes Prediction

1 citations · 1 across the 1 of their papers we have counts for

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8 papers

cs.LG20261 cited

CopulaSMOTE: A Copula-Based Oversampling Approach for Imbalanced Classification in Diabetes Prediction

Agnideep Aich, Md Monzur Murshed, Bruce Wade +1

Class imbalance remains a practical obstacle in the development of clinical prediction models for conditions such as diabetes mellitus, where the number of confirmed cases is often…

stat.ME2026

Comparing Two Categorical Gini Correlations with Applications to Classification Problems

Sameera Hewage, Yongli Sang

This article proposes an inferential framework for comparing predictor importance in classification problems with categorical response variables. The approach is based on the categ…

stat.ME2026

gcor: A Python Implementation of Categorical Gini Correlation and Its Inference

Sameera Hewage

Categorical Gini Correlation (CGC), introduced by Dang et al. (2020), is a novel dependence measure designed to quantify the association between a numerical variable and a categori…

stat.ME2026

Bayesian Inference for Joint Tail Risk in Paired Biomarkers via Archimedean Copulas with Restricted Jeffreys Priors

Agnideep Aich, Md. Monzur Murshed, Sameera Hewage +1

We propose a Bayesian copula-based framework to quantify clinically interpretable joint tail risks from paired continuous biomarkers. After converting each biomarker margin to rank…

stat.ML2026

A Copula Based Supervised Filter for Feature Selection in Diabetes Risk Prediction Using Machine Learning

Agnideep Aich, Md Monzur Murshed, Sameera Hewage +1

Effective feature selection is critical for robust and interpretable predictive modeling in medicine, especially when risk factors matter most in extreme patient strata. Many stand…

stat.ML2026

Bag of Coins: A Statistical Probe into Neural Confidence Structures

Agnideep Aich, Sameera Hewage, Md Monzur Murshed +2

Modern neural networks often produce miscalibrated confidence scores and struggle to detect out-of-distribution (OOD) inputs, while most existing methods post-process outputs witho…