Twigs classifiers based on the Boundary Vectors Machine (BVM): A novel approach for supervised learning

Information Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 121853 - 121853

Published: Jan. 1, 2025

Language: Английский

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Dynamic Balanced Training Regimes: Elevating model performance through iterative training with imbalanced superset and balanced subset alternation

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126423 - 126423

Published: Jan. 1, 2025

Language: Английский

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A Two-Stage Graph Spatiotemporal Model with Domain-Class Alignment for Fault Diagnosis Under Multi-Source Long-Tailed Distributions

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113698 - 113698

Published: May 1, 2025

Language: Английский

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An Undersampling Method Approaching the Ideal Classification Boundary for Imbalance Problems

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(13), P. 5421 - 5421

Published: June 22, 2024

Data imbalance is a common problem in most practical classification applications of machine learning, and it may lead to results that are biased towards the majority class if not dealt with properly. An effective means solving this undersampling borderline area; however, difficult find area fits boundary. In paper, we present novel framework, whereby clustering samples conducted segmentation then performed boundary according clusters obtained; enables better shape be obtained via performance random sampling these segments. addition, hypothesize there exists an optimal number classifiers integrated into method ensemble learning utilizes multiple have been promote algorithm. After passing hypothesis test, apply improved algorithm newly developed method. The experimental show proposed works well.

Language: Английский

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GQEO: Nearest Neighbor Graph-based Generalized Quadrilateral Element Oversampling for Class-imbalance Problem

Neural Networks, Journal Year: 2024, Volume and Issue: 184, P. 107107 - 107107

Published: Dec. 27, 2024

Language: Английский

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A multimodal data generation method for imbalanced classification with dual-discriminator constrained diffusion model and adaptive sample selection strategy

Information Fusion, Journal Year: 2024, Volume and Issue: 117, P. 102843 - 102843

Published: Dec. 5, 2024

Language: Английский

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Geometric Relative Margin Machine for Heterogeneous Distribution and Imbalanced Classification

Published: Jan. 1, 2024

Class imbalance and heterogeneous data distribution pose significant challenges in classification tasks across various real-world applications. Addressing these issues, this paper introduces the Geometric Relative Margin Machine (GRMM), a novel model that innovatively merges strategies of with advanced adjustment techniques. GRMM is specifically designed to effectively manage dual class heterogeneity. Empirical evaluations on benchmark datasets practical scenarios reveal not only significantly improves accuracy but also enhances robustness against diverse distributions. This study underscores efficacy navigating complexities varied sizes distributions, showcasing its potential as superior tool for complex problems.

Language: Английский

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Multi-oversampling with Evidence Fusion for Imbalanced Data Classification

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 68 - 77

Published: Jan. 1, 2024

Language: Английский

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Data Entropy-Based Imbalanced Learning

Communications in computer and information science, Journal Year: 2024, Volume and Issue: unknown, P. 95 - 109

Published: Jan. 1, 2024

Language: Английский

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Geometric relative margin machine for heterogeneous distribution and imbalanced classification

Information Sciences, Journal Year: 2024, Volume and Issue: 689, P. 121430 - 121430

Published: Sept. 7, 2024

Language: Английский

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