A Closest Resemblance Classifier with Feature Interval Learning and Outranking Measures for Improved Performance by Nabil Belacel

“…Classifiers today face numerous challenges, including overfitting, high computational costs, low accuracy, imbalanced datasets, and lack of interpretability. Additionally, traditional methods often struggle with noisy or missing data. …”
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A cascading decision system for enhanced anomaly classification of large-scale photovoltaic systems using Drone's thermal data with class-imbalance problem by Zoubir Barraz, Imane Sebari, Nassim Lamrini, Kenza Ait El Kadi, Ibtihal Ait Abdelmoula

“…The increasing use of Unmanned Aerial Vehicles (UAVs) for thermal imaging in the inspection of large-scale photovoltaic (PV) installations underscores the challenge of detecting anomalies within imbalanced image datasets, where normal instances significantly outnumber anomalies. …”
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Impact of non-cancer related malnutrition on bone metabolism by Valeria Maccauro, Valerio Spuntarelli, Esmeralda Capristo

“…Malnutrition, defined as an imbalanced nutritional intake due to different causes, still represents an open issue for clinicians. …”
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Application of Big Data Unbalanced Classification Algorithm in Credit Risk Analysis of Insurance Companies by Xian Wu, Huan Liu

“…Propose an integrated learning model based on imbalanced dataset resampling and apply it to UCI dataset (University of California Irvine). …”
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Dynamic arithmetic optimization algorithm control of distributed generations for demand balancing and enhancing power quality of unbalanced distribution systems by Ahmad Eid, Abdulrahman Alsafrani

“…The PSO and DAOA algorithms optimize the three imbalanced distribution systems at full load and throughout the day. …”
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Handling Imbalance Classification Virtual Screening Big Data Using Machine Learning Algorithms by Sahar K. Hussin, Salah M. Abdelmageid, Adel Alkhalil, Yasser M. Omar, Mahmoud I. Marie, Rabie A. Ramadan

“…For a dataset identified without considering the data’s imbalanced nature, most classification methods tend to have high predictive accuracy for the majority category. …”
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Wind energy system fault classification using deep CNN and improved PSO‐tuned extreme gradient boosting by Chun‐Yao Lee, Edu Daryl C. Maceren

“…The effectiveness of the proposed method is demonstrated through validation conducted on a different imbalanced dataset showing superior performance metrics in terms of accuracy. …”
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Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models by Zheng Han

“…Challenges for credit card fraud monitoring include highly imbalanced datasets and the need for advanced models to detect fraud patterns. …”
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Optimizing Parkinson’s Disease Prediction: A Comparative Analysis of Data Aggregation Methods Using Multiple Voice Recordings via an Automated Artificial Intelligence Pipeline by Zhengxiao Yang, Hao Zhou, Sudesh Srivastav, Jeffrey G. Shaffer, Kuukua E. Abraham, Samuel M. Naandam, Samuel Kakraba

“…These findings provide meaningful implications for the further exploration of grouped imbalanced data.…”
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Adaptive Freeshape Clustering for Balanced Energy Saving in the WirelessHART Networks by Guangbing Xiao, Jiamin Shi, Ning Sun, Yong Chen, Yong Zhang

“…The frequent data convergecasting from the sensor nodes to the gateways may cause imbalanced energy consumption in the Wireless Highway Addressable Remote Transducer (WirelessHART) networks, yielding a short network lifetime and frequent failures in the data acquisition. …”
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Decision Tree Ensembles to Predict Coronavirus Disease 2019 Infection: A Comparative Study by Amir Ahmad, Ourooj Safi, Sharaf Malebary, Sami Alesawi, Entisar Alkayal

“…Different performance measures suggest that decision tree ensembles developed for imbalanced datasets perform better. Results also suggest that including age as a variable can improve the performance of various ensembles of decision trees.…”
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Poly(A)-DG: A deep-learning-based domain generalization method to identify cross-species Poly(A) signal without prior knowledge from target species. by Yumin Zheng, Haohan Wang, Yang Zhang, Xin Gao, Eric P Xing, Min Xu

“…Moreover, we test our method against insufficient data and imbalanced data issues and demonstrate that Poly(A)-DG not only outperforms state-of-the-art methods but also maintains relatively high accuracy when it comes to a smaller or imbalanced training set.…”
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Mitigation of Harmonics and Unbalanced Voltage Disturbance Compensation by MSVPWM Based- DVR in the Distribution Net by Haider Muhamad Husen

“…Voltage dip, voltage rise, imbalanced voltage, line notching and distortion of harmonics are problems of power quality frequently take place. …”
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Func-Bagging: An Ensemble Learning Strategy for Improving the Performance of Heterogeneous Anomaly Detection Models by Ruinan Qiu, Yongfeng Yin, Qingran Su, Tianyi Guan

“…Therefore, func-bagging is particularly effective in scenarios with class imbalance and is applicable to classification tasks with imbalanced classes, such as anomaly detection.…”
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The Role of T Cells and Macrophages in Asthma Pathogenesis: A New Perspective on Mutual Crosstalk by Xueyi Zhu, Jie Cui, La Yi, Jingjing Qin, Wuniqiemu Tulake, Fangzhou Teng, Weifeng Tang, Ying Wei, Jingcheng Dong

“…Furthermore, crosstalk between them continuously transmits proinflammatory or anti-inflammatory signals, causing the immune cell activation or repression in the immune response. Consequently, the imbalanced immune microenvironment is the major cause of the exacerbation of asthma. …”
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Enhancing Cluster Accuracy in Diabetes Multimorbidity With Dirichlet Process Mixture Models by Francis John Kita, Srinivasa Rao Gaddes, Peter Josephat Kirigiti

“…Results show that DPMM consistently outperforms traditional methods in clustering high-dimensional and imbalanced datasets, offering significant translational potential for guiding tailored healthcare strategies for complex chronic diseases and optimizing healthcare resource allocation.…”
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An overlapping sliding window and combined features based emotion recognition system for EEG signals by Shruti Garg, Rahul Kumar Patro, Soumyajit Behera, Neha Prerna Tigga, Ranjita Pandey

“…Originality/value – The present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. …”
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Deep CNN ResNet-18 based model with attention and transfer learning for Alzheimer's disease detection by Sofia Biju Francis, Sofia Biju Francis, Jai Prakash Verma

“…Transfer learning is most effective with small, imbalanced datasets, and pre-trained models with SE blocks perform better than others. …”
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Penerapan Text Augmentation untuk Mengatasi Data yang Tidak Seimbang pada Klasifikasi Teks Berbahasa Indonesia by Iftitah Athiyyah Rahma, Lya Hulliyyatus Suadaa

“…Imbalanced data affects the performance and accuracy of the model because the model only focuses on the majority label data. …”
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Enhancing generalization in a Kawasaki Disease prediction model using data augmentation: Cross-validation of patients from two major hospitals in Taiwan. by Chuan-Sheng Hung, Chun-Hung Richard Lin, Jain-Shing Liu, Shi-Huang Chen, Tsung-Chi Hung, Chih-Min Tsai

“…These findings lay a solid foundation for advancing disease prediction in the context of imbalanced medical data.…”
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