Meta-EA: a gene-specific combination of available computational tools for predicting missense variant effects by Panagiotis Katsonis, Olivier Lichtarge

“…Incorporating the effects of splicing and the allele frequency of human polymorphisms further enhances the performance of Meta-EA, achieving an area under the receiver operating characteristic curve of 0.97 for both gene-balanced and imbalanced clinical assessments. In conclusion, this work leverages the wealth of existing variant impact prediction approaches to generate improved estimations for clinical interpretation.…”
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Machine Learning-Based Alzheimer’s Disease Stage Diagnosis Utilizing Blood Gene Expression and Clinical Data: A Comparative Investigation by Manash Sarma, Subarna Chatterjee

“…The results indicated that our multiclassification model effectively manages the imbalanced data of a high-dimension, low-sample-size (HDLSS) nature to identify samples of the minority class. …”
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Easy Data Augmentation untuk Data yang Imbalance pada Konsultasi Kesehatan Daring by Anisa Nur Azizah, Misbachul Falach Asy'ari, Ifnu Wisma Dwi Prastya, Diana Purwitasari

“…Abstract   The text augmentation approach is often utilized for handling imbalanced data of classifying text corpus, such as online health consultation (OHC) texts, i.e., alodokter.com. …”
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Real-time event detection using recurrent neural network in social sensors by Van Quan Nguyen, Tien Nguyen Anh, Hyung-Jeong Yang

“…The proposed method shows no size limitation, supplementation of more embeddings than standard multichannel based approaches, and obtained similar performance (accuracy score) on some benchmark data sets, especially in an imbalanced data set. For event detection, a long short-term memory network is used as a predictor that learns higher level temporal features so as to predict future values. …”
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Network intrusion detection method based on VAE-CWGAN and fusion of statistical importance of feature by Taotao LIU, Yu FU, Kun WANG, Xueyuan DUAN

“…Considering the problems of traditional intrusion detection methods limited by the class imbalance of datasets and the poor representation of selected features, a detection method based on VAE-CWGAN and fusion of statistical importance of features was proposed.Firstly, data preprocessing was conducted to enhance data quality.Secondly, a VAE-CWGAN model was constructed to generate new samples, addressing the problem of imbalanced datasets, ensuring that the classification model no longer biased towards the majority class.Next, standard deviation, difference of median and mean were used to rank the features and fusion their statistical importance for feature selection, aiming to obtain more representative features, which made the model can better learn data information.Finally, the mixed data set after feature selection was classified through a one-dimensional convolutional neural network.Experimental results show that the proposed method demonstrates good performance advantages on three datasets, namely NSL-KDD, UNSW-NB15, and CIC-IDS-2017.The accuracy rates are 98.95%, 96.24%, and 99.92%, respectively, effectively improving the performance of intrusion detection.…”
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Fault diagnosis method of timing signal based on Nadam-TimeGAN and XGBoost by HEI Xinhong, GAO Miao, ZHANG Kuan, FEI Rong, QIU Yuan, JI Wenjiang

“…The experimental results validate the effectiveness and generalization of the proposed method for imbalanced data fault diagnosis.…”
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Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques. by Hilman Nordin, Bushroa Abdul Razak, Norrima Mokhtar, Mohd Fadzil Jamaludin, Adeel Mehmood

“…However, the CART algorithm exhibits superior performance, increasing precision by 32% to 53% while maintaining high accuracy (96%) even with an imbalanced dataset. This innovative method has the potential to transform the approach to managing mold defects in fine art paintings by offering a more precise and efficient means of identification. …”
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The Use of Machine Learning Methods in Road Safety Research in Poland by Anna Borucka, Sebastian Sobczuk

“…The best choice for imbalanced data, especially when the goal is to identify rare classes, is the RF model. …”
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The Response of Ionospheric Currents to External Drivers Investigated Using a Neural Network‐Based Model by Xin Cao, Xiangning Chu, Jacob Bortnik, James M. Weygand, Jinxing Li, Homayon Aryan, Donglai Ma

“…To study the response of ionospheric currents to external drivers including geomagnetic indices and solar radiation, we developed a feedforward neural network model trained on the Equivalent Ionospheric Current (EIC) data from 1st January 2007 to 31st December 2019. Due to the highly imbalanced nature of the ionospheric currents data, which means that the data of extreme events are much less than those of quiet times, we utilized different loss functions to improve the model performance. …”
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RS-FeatFuseNet: An Integrated Remote Sensing Object Detection Model with Enhanced Feature Extraction by Yijuan Qiu, Jiefeng Xue, Gang Zhang, Xuying Hao, Tao Lei, Ping Jiang

“…During training, we utilized focal loss to handle the issue of imbalanced target class distributions in remote sensing datasets, improving the detection accuracy of challenging objects. …”
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A comprehensive review of the roles of T-cell immunity in preeclampsia by Xu Peng, Ibeh Chinwe Oluchi-Amaka, Joanne Kwak-Kim, Joanne Kwak-Kim, Xiuhua Yang

“…PE has been associated with a range of immune disorders, including a preponderance of T helper (Th) 1 over Th2 cells and imbalanced levels of Th17 and T regulatory cells (Tregs). …”
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Flight Delay Classification Prediction Based on Stacking Algorithm by Jia Yi, Honghai Zhang, Hao Liu, Gang Zhong, Guiyi Li

“…In this research, the principle of the Stacking classification algorithm is introduced, the SMOTE algorithm is selected to process imbalanced datasets, and the Boruta algorithm is utilized for feature selection. …”
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Generative Elastic Networks (GENs) and Application on Classification of Single-Lead Electrocardiogram by Nan Xiao, Kun Zhao, Hao Zhang

“…The essence of GEN lies in filtering and recording key samples and their mapping processes, showcasing inherent adaptability to small and imbalanced dataset. Leveraging the MIT-BIH Arrhythmia Database, by six classes of single-lead ECG with typical shape characteristics are classified. …”
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Comparative analysis of FACT devices for optimal improvement of power quality in unbalanced distribution systems by Ahmed M. Elkholy, Dmitry I. Panfilov, Ahmed E. ELGebaly

“…The performance of the IEEE-13 bus imbalanced distribution model is investigated using the Newton-Raphson method. …”
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ResInceptNet-SA: A Network Traffic Intrusion Detection Model Fusing Feature Selection and Balanced Datasets by Guorui Liu, Tianlin Zhang, Hualin Dai, Xinyang Cheng, Daoxuan Yang

“…This model enhances the accuracy of abnormal network traffic detection and outperforms existing models when applied to imbalanced datasets, offering a new solution for network traffic intrusion detection.…”
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Prediction of Defective Software Modules Using Class Imbalance Learning by Divya Tomar, Sonali Agarwal

“…The learning process of a software defect predictor is difficult due to the imbalanced distribution of software modules between defective and nondefective classes. …”
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Bio-Inspired Algorithms for Efficient Clustering and Routing in Flying Ad Hoc Networks by Juhi Agrawal, Muhammad Yeasir Arafat

“…For cluster maintenance, a congestion-based approach redistributes UAVs in overloaded or imbalanced clusters. The AO-based routing algorithm ensures reliable data transmission from CHs to the base station by leveraging predictive mobility data, load balancing, fault tolerance, and global insights from ferry nodes. …”
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User scheduling and power allocation strategy for cell-free networks based on federated learning by WANG Huahua, HUANG Yexia, LI Ling

“…In order to address the issue of limited training performance in federated learning (FL) due to user link quality disparities and imbalanced communication, and computing resource utilization in cell-free network systems, a joint optimization problem for user scheduling and power allocation was designed. …”
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HARD-VOTING DAN SOFT-VOTING CLASSIFIER: MODEL KLASIFIKASI RISIKO KEMATIAN PADA PASIEN GAGAL GINJAL KRONIK by LUTHFATUL AMALIANA, ANI BUDI ASTUTI, ROSSANDA SEVIA GADIS, NAURAH ATIKAH RABBANI, NABILA AYUNDA SOVIA

“…The voting classifier is used to combine predictions from several classification models, where hard-voting makes decisions based on the majority vote, and soft-voting considers the average prediction probability. However, with imbalanced data, classification models tend to be biased toward the majority class. …”
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A comparative analysis of LSTM models aided with attention and squeeze and excitation blocks for activity recognition by Murad Khan, Yousef Hossni

“…Current challenges in HAR systems include variability in sensor data influenced by factors like sensor placement, user differences, and environmental conditions. Additionally, imbalanced datasets and computational complexity hinder the performance of these systems in real-world applications. …”
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