Network-based instance hardness measures for classification problems DOI

Gustavo P. Torquette,

Márcio P. Basgalupp, Teresa B. Ludermir

et al.

Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, Journal Year: 2025, Volume and Issue: unknown, P. 1196 - 1203

Published: March 31, 2025

Language: Английский

Resampling approaches to handle class imbalance: a review from a data perspective DOI Creative Commons
Miguel Martins Carvalho, Armando J. Pinho, Susana Brás

et al.

Journal Of Big Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: March 23, 2025

Language: Английский

Citations

0
Enhancing bridge inspection data quality using machine learning DOI
Chenhong Zhang, Xiaoming Lei, Ye Xia

et al.

Automation in Construction, Journal Year: 2025, Volume and Issue: 175, P. 106182 - 106182

Published: April 12, 2025

Language: Английский

Citations

0
MWMOTE-FRIS-INFFC: An Improved Majority Weighted Minority Oversampling Technique for Solving Noisy and Imbalanced Classification Datasets DOI Creative Commons
Dong Zhang, Xiang Huang, Gen Li

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(9), P. 4670 - 4670

Published: April 23, 2025

In view of the data fault diagnosis and good product testing in industrial field, high-noise unbalanced samples exist widely, such are very difficult to analyze field analysis. The oversampling technique has proved be a simple solution past, but it no significant resistance noise. order solve binary classification problem data, an enhanced majority-weighted minority technique, MWMOTE-FRIS-INFFC, is introduced this study, which specially used for processing noise-unbalanced classified sets. method uses Euclidean distance assign sample weights, synthesizes combines new into with larger weights belonging few classes, thus solves scarcity smaller class clusters. Then, fuzzy rough instance selection (FRIS) eliminate subsets synthetic low clustering membership, effectively reduces overfitting tendency caused by oversampling. addition, integration fusion iterative filters (INFFC) helps mitigate noise issues, both raw On basis, series experiments designed improve performance 6 algorithms on 8 sets using MWMOTE-FRIS-INFFC algorithm proposed paper.

Language: Английский

Citations

0
Investigating the impact of balancing, filtering, and complexity on predictive multiplicity: A data-centric perspective DOI
Mustafa Çavuş, Przemysław Biecek

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 103243 - 103243

Published: May 1, 2025

Language: Английский

Citations

0
Network-based instance hardness measures for classification problems DOI

Gustavo P. Torquette,

Márcio P. Basgalupp, Teresa B. Ludermir

et al.

Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, Journal Year: 2025, Volume and Issue: unknown, P. 1196 - 1203

Published: March 31, 2025

Language: Английский

Citations

0