Cardiovascular Disease Detection through Innovative Imbalanced Learning and AUC Optimization by Karthikeyan Palanisamy, Krishnaveni Krishnasamy, Praba Venkadasamy

“…In this paper, we introduce a novel imbalanced learning approach named Imbalanced Maximizing-Area Under the Curve (AUC) Proximal Support Vector Machine (ImAUC-PSVM), which harnesses the foundational principles of traditional PSVM for the detection of CVDs. …”
Get full text

Evaluation of the practical application of the category-imbalanced myeloid cell classification model. by Zhigang Hu, Aoru Ge, Xinzheng Wang, Cuisi Ou, Shen Wang, Junwen Wang

Get full text

About the confusion-matrix-based assessment of the results of imbalanced data classification by V. V. Starovoitov, Yu. I. Golub

Subjects: Get full text

A novel oversampling method based on Wasserstein CGAN for imbalanced classification by Hongfang Zhou, Heng Pan, Kangyun Zheng, Zongling Wu, Qingyu Xiang

Subjects: Get full text

A Deep-Learning Prediction Model for Imbalanced Time Series Data Forecasting by Chenyu Hou, Jiawei Wu, Bin Cao, Jing Fan

Subjects: Get full text

Surprise Bug Report Prediction Utilizing Optimized Integration with Imbalanced Learning Strategy by Hui Li, Yang Qu, Shikai Guo, Guofeng Gao, Rong Chen, Guo Chen

“…The main reason is that surprise bugs only occupy a small percentage of all the bugs, and it is difficult to identify these surprise bugs from the imbalanced distribution. In order to overcome the imbalanced category distribution of the bugs, a method based on machine learning to predict surprise bugs is presented in this paper. …”
Get full text

Adaptive Control for Improved Virtual Synchronous Generator Under Imbalanced Grid Voltage by Yangyang Chen, Wei Han, Youhao Hu, Yilin Zhang

Subjects: Get full text

Imbalanced Data Sets Classification Based on SVM for Sand-Dust Storm Warning by Yonghua Xie, Yurong Liu, Qingqiu Fu

“…In view of the SVM classification for the imbalanced sand-dust storm data sets, this paper proposes a hybrid self-adaptive sampling method named SRU-AIBSMOTE algorithm. …”
Get full text

Distribution and Evolution of Chorus Waves Modeled by a Neural Network: The Importance of Imbalanced Regression by Xiangning Chu, Jacob Bortnik, Wen Li, Xiao‐Chen Shen, Qianli Ma, Donglai Ma, David Malaspina, Sheng Huang

Subjects: Get full text

A novel method for power transformer fault diagnosis considering imbalanced data samples by Jun Chen, Yong Wang, Lingming Kong, Yilong Chen, Mianzhi Chen, Qian Cai, Gehao Sheng

Subjects: Get full text

Corrigendum to “Imbalanced Regional Development of Acute Ischemic Stroke Care in Emergency Departments in China” by Jianguo Li, Jingming Liu, Yuefeng Ma, Peng Peng, Xiaojun He, Wei Guo

Get full text

IMBoost: A New Weighting Factor for Boosting to Improve the Classification Performance of Imbalanced Data by SeyedEhsan Roshan, Jafar Tanha, Farzad Hallaji, Mohammad-reza Ghanbari

“…Imbalanced datasets pose significant challenges in the field of machine learning, as they consist of samples where one class (majority) dominates over the other class (minority). …”
Get full text

Mass Cytometry Reveals the Imbalanced Immune State in the Peripheral Blood of Patients with Essential Hypertension by Rui Yang, Yuhong He, Honggang Zhang, Qiuju Zhang, Bingwei Li, Changming Xiong, Yubao Zou, Bingyang Liu

“…In conclusion, the altered number and antigen expression of immune cells reflect the imbalanced immune state of the peripheral blood in patients with EH.…”
Get full text

NATE: Non-pArameTric approach for Explainable credit scoring on imbalanced class. by Seongil Han, Haemin Jung

“…NATE incorporates class balancing methods to mitigate the impact of imbalanced data distributions and integrates interpretability features to elucidate the model's decision-making process. …”
Get full text

A Multi-Index Generative Adversarial Network for Tool Wear Detection with Imbalanced Data by Guokai Zhang, Haoping Xiao, Jingwen Jiang, Qinyuan Liu, Yimo Liu, Liying Wang

“…The scarcity of abnormal data leads to imbalanced data in the field of monitoring tool wear conditions. …”
Get full text

Imbalanced Fault Classification of Bearing via Wasserstein Generative Adversarial Networks with Gradient Penalty by Baokun Han, Sixiang Jia, Guifang Liu, Jinrui Wang

“…Recently, generative adversarial networks (GANs) are widely applied to increase the amounts of imbalanced input samples in fault diagnosis. However, the existing GAN-based methods have convergence difficulties and training instability, which affect the fault diagnosis efficiency. …”
Get full text

HSDP: A Hybrid Sampling Method for Imbalanced Big Data Based on Data Partition by Liping Chen, Jiabao Jiang, Yong Zhang

“…The classical classifiers are ineffective in dealing with the problem of imbalanced big dataset classification. Resampling the datasets and balancing samples distribution before training the classifier is one of the most popular approaches to resolve this problem. …”
Get full text

Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets by Inam Abousaber, Haitham F. Abdallah, Hany El-Ghaish

Subjects: Get full text

MKC-SMOTE: A Novel Synthetic Oversampling Method for Multi-Class Imbalanced Data Classification by Jiao Wang, Norhashidah Awang

Subjects: “…Multi-class imbalanced dataset…”
Get full text

A stacked ensemble approach with resampling techniques for highly effective fraud detection in imbalanced datasets by Idongesit E. Eteng, Udeze L. Chinedu, Ayei E. Ibor

Subjects: “…Imbalanced dataset…”
Get full text