Enhancing stroke risk prediction through class balancing and data augmentation with CBDA-ResNet50 by Muhammad Asim Saleem, Ashir Javeed, Wasan Akarathanawat, Aurauma Chutinet, Nijasri Charnnarong Suwanwela, Pasu Kaewplung, Surachai Chaitusaney, Watit Benjapolakul

“…In most cases, these problems lead to biased or less reliable predictions. …”
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Improved LightGBM for Extremely Imbalanced Data and Application to Credit Card Fraud Detection by Xiaosong Zhao, Yong Liu, Qiangfu Zhao

“…The new approaches combine class balancing or oversampling technology with LightGBM to solve the EID problem comprehensively. …”
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A PCC-Ensemble-TCN model for wind turbine icing detection using class-imbalanced and label-missing SCADA data by Shenyi Ding, Zhijie Wang, Jue Zhang, Fang Han, Xiaochun Gu, Guangxiao Song

“…Aiming at the class-imbalance problem, this article constructs multiple class-balanced subsets from the original dataset by under-sampling the normal data. …”
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A spatial bearing fault classification method based on improved APSMOTE-WKMFA by CHEN Chao, YANG Chenhao, XU Haosen, WAN Ouying, HAN Liling

“…Aiming at the problem of poor classification performance of a model for a few classes of fault samples in the case of category imbalance, the spatial bearing fault classification method based on an improved affinity propagation synthetic minority oversampling technique-wavelet kernel marginal Fisher analysis （APSMOTE-WKMFA） was proposed.MethodsFirstly, the geodesic distance was used as the similarity metric for the affinity propagation algorithm, and the synthetic minority oversampling technique （SMOTE） was used to generate samples in the filtered subclusters up to the class balance. …”
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Limits of Solar Flare Forecasting Models and New Deep Learning Approach by G. Francisco, M. Berretti, S. Chierichini, R. Mugatwala, J. Fernandes, T. Barata, D. Del Moro

“…This new framework offers similar information to active region (AR)-based forecasting models while bypassing the problem of unrecorded and misattributed flares that are detrimental to machine learning training. …”
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Evaluating the three-level approach of the U-smile method for imbalanced binary classification. by Barbara Więckowska, Katarzyna B Kubiak, Przemysław Guzik

“…Real-life binary classification problems often involve imbalanced datasets, where the majority class outnumbers the minority class. …”
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Multimodal Framework for Long-Tailed Recognition by Jian Chen, Jianyin Zhao, Jiaojiao Gu, Yufeng Qin, Hong Ji

“…., minority classes occupy most of the data, while most classes have very few samples) is a common problem in image classification. In this paper, we propose a novel multimodal framework for long-tailed data recognition. …”
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Golden eagle optimized CONV-LSTM and non-negativity-constrained autoencoder to support spatial and temporal features in cancer drug response prediction by Wesam Ibrahim Hajim, Suhaila Zainudin, Kauthar Mohd Daud, Khattab Alheeti

“…NNCAE methodology is used after performing the standard pre-processing procedures to handle the noise and class imbalance problem. This class balanced and noise-removed input data features are learned to train the proposed hybrid classifier. …”
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Sample Weighting Methods for Compensating Class Imbalance in Elephant Flow Classification by Piotr Jurkiewicz, Robert Wojcik, Jerzy Domzal

“…These findings provide valuable insights for researchers and practitioners working on flow classification problem, contributing to more efficient network traffic management systems.…”
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STUDI KOMPARASI METODE SVM-SMOTE DAN SMOTE-TOMEK DALAM MENGATASI IMBALANCE CLASS MENGGUNAKAN MODEL XGBOOST PADA KLASIFIKASI RUMAH TANGGA PENERIMA KUR by Eka Dicky Darmawan Yanuari, Rachmat Bintang Yudhianto, Ratu Risha Ulfia, Bagus Sartono, Aulia Rizki Firdawanti

“…This study aims to compare the SMOTE, SVM-SMOTE, and SMOTE-Tomek methods using the XGBoost model in overcoming the problem of class imbalance and to determine the factors that affect the status of KUR recipients in West Java Province. …”
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Optimal Convolutional Networks for Staging and Detecting of Diabetic Retinopathy by Minyar Sassi Hidri, Adel Hidri, Suleiman Ali Alsaif, Muteeb Alahmari, Eman AlShehri

“…The proposed model combines the use of the ConvNet architecture taken from ImageNet, data augmentation, class balancing, and transfer learning in order to establish a benchmarking test. …”
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Instagram fake profile detection using an ensemble learning method by Bharti Goyal, Nasib Singh Gill, Preeti Gulia, Noha Alduaiji, Piyush Kumar Shukla, Shreyas J

“…Abstract Counterfeit accounts still pose a big problem for Instagram users. Trust is being eroded, and online security is being compromised as a result of these accounts’ constant contribution to Instagram’s spam, harmful information, and deceptive content problems. …”
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Transfer learning based hybrid feature learning framework for enhanced skin cancer diagnosis using deep feature integration by Maridu Bhargavi, Sivadi Balakrishna

“…Skin cancer continues to be a major health problem worldwide, with excessive misdiagnosis of skin cancer among dermatologists resulting in delayed treatment and poor patient outcomes. …”
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Few-Shot Unsupervised Domain Adaptation Based on Refined Bi-Directional Prototypical Contrastive Learning for Cross-Scene Hyperspectral Image Classification by Xuebin Tang, Hanyi Shi, Chunchao Li, Cheng Jiang, Xiaoxiong Zhang, Lingbin Zeng, Xiaolei Zhou

“…We propose an end-to-end refined bi-directional prototypical contrastive learning (RBPCL) framework for overcoming the HSICC problem with only a few labeled samples in the source domain. …”
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Two-dimensional spatial orientation relation recognition between image objects by Gong Peiyong, Zheng Kai, Jiang Yi, Zhao Huixuan, Huai Honghao, Guan Ruijie

“…This study systematically addresses the recognition problem of two-dimensional spatial orientation relations and develops the Target Spatial Orientation Vector Field (TSOVF) algorithm, a novel end-to-end framework to explicitly model spatial orientation dependencies. …”
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GANs for data augmentation with stacked CNN models and XAI for interpretable maize yield prediction by Ishaan Seshukumar Pothapragada, Sujatha R

“…A robust maize yield prediction framework is proposed to counter major problems predominantly present in Agri-tech analytics, like scarcity of data, class imbalance, redundant features, and model interpretability. …”
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Model Klasifikasi Dengan Logistic Regression Dan Recursive Feature Elimination Pada Data Tidak Seimbang by Sutarman, Rimbun Siringoringo, Dedy Arisandi, Edi Kurniawan, Erna Budhiarti Nababan

“…Logistic regression can yield good results in classification and prediction problems. The extensive features of the dataset can lead to computational burdens and reduced classification performance. …”
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