Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning

21 September 2016

Papers citing "Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning"

8 / 8 papers shown

Title
Tree Boosting Methods for Balanced andImbalanced Classification and their Robustness Over Time in Risk Assessment Gissel Velarde Michael Weichert Anuj Deshmunkh Sanjay Deshmane Anindya Sudhir K. Sharma Vaibhav Joshi 110 3 0 25 Apr 2025
Provable Imbalanced Point Clustering David Denisov Dan Feldman Shlomi Dolev Michael Segal 91 0 0 13 Mar 2025
Reproducible Machine Learning-based Voice Pathology Detection: Introducing the Pitch Difference Feature Jan Vrba Jakub Steinbach Tomáš Jirsa Laura Verde Roberta De Fazio ... Lukáš Hájek Zuzana Sedláková Jan Mareš Jan Mareš Noriyasu Homma 40 0 0 14 Oct 2024
Quantifying the Cross-sectoral Intersecting Discrepancies within Multiple Groups Using Latent Class Analysis Towards Fairness Yingfang Yuan Kefan Chen Mehdi Rizvi Lynne Baillie Wei Pang 65 0 0 24 May 2024
Restoring balance: principled under/oversampling of data for optimal classification Emanuele Loffredo Mauro Pastore Simona Cocco R. Monasson 53 9 0 15 May 2024
Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models Zeyu Yang Peikun Guo Khadija Zanna Akane Sano Xiaoxue Yang Akane Sano DiffM 67 9 0 12 Apr 2024
Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants Abdoulaye Sakho Emmanuel Malherbe Erwan Scornet 43 2 0 06 Feb 2024
SMOTE: Synthetic Minority Over-sampling Technique Nitesh Chawla Kevin W. Bowyer Lawrence Hall W. Kegelmeyer AI4TS 252 25,443 0 09 Jun 2011