An imbalanced learning method based on graph tran|smote ...

An imbalanced learning method based on graph tran-smote ... - Nature

The tran-smote on graphs (GTS) method for fraud detection is proposed by this study. Structural features of each type of node are deeply mined using a subgraph ...

(PDF) An imbalanced learning method based on graph tran-smote ...

Due to the scarcity of fraudsters, the conventional graph neural network might overlook or obscure critical fraud information, leading to insufficient ...

Overcoming Class Imbalance with SMOTE - Train in Data's Blog

SMOTE is a type of data augmentation technique that generates new synthetic samples by interpolating between existing minority-class samples.

An imbalanced learning method based on graph tran-smote for ...

An imbalanced learning method based on graph tran-smote for fraud detection. Wen, J.; Tang, X.; Lu, J. Scientific Reports 14(1): 16560. 2024. ISSN/ISBN: 2045 ...

An imbalanced learning method by combining SMOTE with Center ...

In order to improve its performance, this paper proposes an oversampling method called SMOTE-COF, which is an improvement of SMOTE based on center offset factor ...

A graph-based semi-supervised reject inference framework ...

Imbalanced learning, which tries to alter the distribution of different classes in the original training dataset and improve the performance of machine leaning ...

Effective Class-Imbalance Learning Based on SMOTE and ... - MDPI

The classification results demonstrate that the mixed Synthetic Minority Oversampling Technique (SMOTE)-Normalization-CNN outperforms different methodologies ...

ZhiningLiu1998/awesome-imbalanced-learning: Everything ... - GitHub

Failure to account for the class imbalance often causes inaccurate and decreased predictive performance of many classification algorithms. Imbalanced learning ...

Imbalanced graph learning via mixed entropy minimization - Nature

Traditional methods often address this issue through synthetic oversampling techniques for the minority class, which can complicate the training ...

Surrounding neighborhood-based SMOTE for learning from ... - CORE

The issue of class imbalance has been addressed by numerous approaches at both data and algorithmic lev- els [23]. The methods at the algorithmic level modify.

SMOTE for Learning from Imbalanced Data

SMOTE for Learning from Imbalanced Data: 15-year Anniversary. • INNO (Li et al., 2013a) is a technique for graph-based semi-supervised learning and performs ...

A GNN-based Imbalanced Learning Approach for Fraud Detection

Computing methodologies → Neural networks; • Security and privacy → Software and application security. KEYWORDS class imbalance, graph neural network, fraud ...

A GNN-based Imbalanced Learning Approach for Fraud Detection

To remedy the class imbalance problem of graph-based fraud detection, we propose a Pick and Choose Graph Neural Network (PC-GNN for short) for imbalanced ...

Oversampling — Handling Imbalanced Data | by Abdallah Ashraf

3. SMOTE ... Which stands for Synthetic Minority Oversampling Technique, is a widely used oversampling method for mitigating class imbalance ...

Resampling strategies for imbalanced datasets - Kaggle

SMOTE (Synthetic Minority Oversampling TEchnique) consists of synthesizing elements for the minority class, based on those that already exist. It works randomly ...

A Hypergraph-based Oversampling Approach for Imbalanced Node ...

Inspired by SMOTE concept, we propose HyperSMOTE as a solution to alleviate the class imbalance issue in hypergraph learning. This method ...

Class-Imbalanced Graph Learning without Class Rebalancing

Class imbalance is prevalent in real-world node classification tasks and poses great challenges for graph machine-learning models.

A theoretical distribution analysis of synthetic minority oversampling ...

The SMOTE method generates new synthetic data patterns by performing linear interpolation between minority class samples and their K nearest ...

SMOTE for Imbalanced Classification with Python

The SMOTE class acts like a data transform object from scikit-learn in that it must be defined and configured, fit on a dataset, then applied to ...

A GNN-based Imbalanced Learning Approach for Fraud Detection

240 Citations · An imbalanced learning method based on graph tran-smote for fraud detection · Cost-Sensitive GNN-Based Imbalanced Learning for Mobile Social ...