Decision support systems are essential for maintaining grid stability in low-carbon power systems, such as wind power plants, by providing real-time alerts to control room operators regarding potential events, including Wind Power Ramp Events (WPREs). These early warnings enable the timely initiation of more detailed system stability assessments and preventive actions. However, forecasting these events is challenging due to the inherent class imbalance in WPRE datasets, where ramp events are less frequent (typically less than 15\% of observed events) compared to normal conditions. Ignoring this characteristic undermines the performance of conventional machine learning models, which often favor the majority class. This paper introduces a novel methodology for WPRE forecasting as a multivariate time series classification task and proposes a data preprocessing strategy that extracts features from recent power observations and masks unavailable ramp information, making it integrable with traditional real-time ramp identification tools. Particularly, the proposed methodology combines majority-class undersampling and ensemble learning to enhance wind ramp event forecasting under class imbalance. Numerical simulations conducted on a real-world dataset demonstrate the superiority of our approach, achieving over 85% accuracy and 88% weighted F1 score, outperforming benchmark classifiers.
Adaptive kernel-density approach for imbalanced binary classification
Graduate School of Science and Technology, Nara Institute of Science and Technology, Japan（科学技术研究生院，科学与技术国立研究所，日本）；Data Science Center, Nara Institute of Science and Technology, Japan（数据科学中心，科学与技术国立研究所，日本）
Tencent AI Lab（腾讯人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；Tsinghua University（清华大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Hong Kong University of Science and Technology（香港科技大学）；Renmin University of China（中国人民大学）；University of Science and Technology of China（中国科学技术大学）
The last decade has witnessed a prosperous development of computational methods and dataset curation for AI-aided drug discovery (AIDD). However, real-world pharmaceutical datasets often exhibit highly imbalanced distribution, which is overlooked by the current literature but may severely compromise the fairness and generalization of machine learning applications. Motivated by this observation, we introduce ImDrug, a comprehensive benchmark with an open-source Python library which consists of 4 imbalance settings, 11 AI-ready datasets, 54 learning tasks and 16 baseline algorithms tailored for …
National Digital Switching System Engineering and Technological Research Center（国家数字交换系统工程技术研究中心）；University of Toronto（多伦多大学）；Fudan University（复旦大学）
With the rapid development of mobile networks, the people's social contacts have been considerably facilitated. However, the rise of mobile social network fraud upon those networks, has caused a great deal of distress, in case of depleting personal and social wealth, then potentially doing significant economic harm. To detect fraudulent users, call detail record (CDR) data, which portrays the social behavior of users in mobile networks, has been widely utilized. But the imbalance problem in the aforementioned data, which could severely hinder the effectiveness of fraud detectors based on graph…
Towson University（陶森大学）；University of Connecticut（康涅狄格大学）
Classification predictive modeling involves the accurate assignment of observations in a dataset to target classes or categories. There is an increasing growth of real-world classification problems with severely imbalanced class distributions. In this case, minority classes have much fewer observations to learn from than those from majority classes. Despite this sparsity, a minority class is often considered the more interesting class yet developing a scientific learning algorithm suitable for the observations presents countless challenges. In this article, we suggest a novel multi-class class…
School of Vehicle and Mobility, Tsinghua University（清华大学车辆与学院）
Merging into the highway from the on-ramp is an essential scenario for automated driving. The decision-making under the scenario needs to balance the safety and efficiency performance to optimize a long-term objective, which is challenging due to the dynamic, stochastic, and adversarial characteristics. The Rule-based methods often lead to conservative driving on this task while the learning-based methods have difficulties meeting the safety requirements. In this paper, we propose an RL-based end-to-end decision-making method under a framework of offline training and online correction, called …
University of Oxford（牛津大学）；John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust（牛津大学医院NHS基金会信托约翰·拉德克利夫医院）
With the rapid growth of memory and computing power, datasets are becoming increasingly complex and imbalanced. This is especially severe in the context of clinical data, where there may be one rare event for many cases in the majority class. We introduce an imbalanced classification framework, based on reinforcement learning, for training extremely imbalanced data sets, and extend it for use in multi-class settings. We combine dueling and double deep Q-learning architectures, and formulate a custom reward function and episode-training procedure, specifically with the added capability of handl…
L3S Research Center（L3S研究中心）；Leibniz University of Hanover（汉诺威莱布尼茨大学）
Online class imbalance learning deals with data streams that are affected by both concept drift and class imbalance. Online learning tries to find a trade-off between exploiting previously learned information and incorporating new information into the model. This requires both the incremental update of the model and the ability to unlearn outdated information. The improper use of unlearning, however, can lead to the retroactive interference problem, a phenomenon that occurs when newly learned information interferes with the old information and impedes the recall of previously learned informati…
Exploring Data Augmentation and Resampling Strategies for Transformer-Based Models to Address Class Imbalance in AI Scoring of Scientific Explanations in NGSS Classroom
Michigan State University（密歇根州立大学）；CREATE for STEM（STEM创新计划）；University of Houston（休斯顿大学）
Tel Aviv University（特拉维夫大学）；Playtika Ltd.（Playtika公司）；Bar-Ilan University（巴伊兰大学）
When presented with a binary classification problem where the data exhibits severe class imbalance, most standard predictive methods may fail to accurately model the minority class. We present a model based on Generative Adversarial Networks which uses additional regularization losses to map majority samples to corresponding synthetic minority samples. This translation mechanism encourages the synthesized samples to be close to the class boundary. Furthermore, we explore a selection criterion to retain the most useful of the synthesized samples. Experimental results using several downstream cl…
Geometric Imbalance in Semi-Supervised Node Classification
Fudan University（复旦大学）；Shanghai Innovation Institute（上海创新研究院）；MBZUAI；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Logs AI Project（Logs AI项目）
Semi-Supervised Learning (SSL) has achieved great success in overcoming the difficulties of labeling and making full use of unlabeled data. However, SSL has a limited assumption that the numbers of samples in different classes are balanced, and many SSL algorithms show lower performance for the datasets with the imbalanced class distribution. In this paper, we introduce a task of class-imbalanced semi-supervised learning (CISSL), which refers to semi-supervised learning with class-imbalanced data. In doing so, we consider class imbalance in both labeled and unlabeled sets. First, we analyze ex…
Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning
Interdisciplinary Program in Artificial Intelligence, Seoul National University, Republic of Korea（人工智能跨学科项目，首尔国立大学，韩国）；Department of Nuclear Medicine, Seoul National University College of Medicine, Republic of Korea（核医学系，首尔国立大学医学院，韩国）；Brightonix Imaging Inc. Seoul, Republic of Korea（Brightonix影像公司，首尔，韩国）
Evaluation of AutoML Frameworks for IDS under Imbalanced Data Conditions of the NSL-KDD Dataset
Cybersecurity and Artificial Intelligence Laboratory (CS&I Lab), National Institute of Telecommunications (Inatel)（网络安全与人工智能实验室（CS&I Lab），国家电信研究所（Inatel））；Wireless and Artificial Intelligence Laboratory (WAI Lab), National Institute of Telecommunications (Inatel)（无线与人工智能实验室（WAI Lab），国家电信研究所（Inatel））
INO-SGD: Addressing Utility Imbalance under Individualized Differential Privacy
Department of Computer Science, National University of Singapore（新加坡国立大学计算机科学系）；Agency for Science, Technology and Research (A*STAR), Singapore（新加坡科技研究局）
When faced with severely imbalanced binary classification problems, we often train models on bootstrapped data in which the number of instances of each class occur in a more favorable ratio, e.g., one. We view algorithmic inequity through the lens of imbalanced classification: in order to balance the performance of a classifier across groups, we can bootstrap to achieve training sets that are balanced with respect to both labels and group identity. For an example problem with severe class imbalance---prediction of suicide death from administrative patient records---we illustrate how an equity-…
Data imbalance remains one of the most widespread problems affecting contemporary machine learning. The negative effect data imbalance can have on the traditional learning algorithms is most severe in combination with other dataset difficulty factors, such as small disjuncts, presence of outliers and insufficient number of training observations. Aforementioned difficulty factors can also limit the applicability of some of the methods of dealing with data imbalance, in particular the neighborhood-based oversampling algorithms based on SMOTE. Radial-Based Oversampling (RBO) was previously propos…
We address the problem of severe class imbalance in unsupervised domain adaptation, when the class spaces in source and target domains diverge considerably. Till recently, domain adaptation methods assumed the aligned class spaces, such that reducing distribution divergence makes the transfer between domains easier. Such an alignment assumption is invalidated in real world scenarios where some source classes are often under-represented or simply absent in the target domain. We revise the current approaches to class imbalance and propose a new one that uses latent codes in the adversarial domai…
University of Amsterdam（阿姆斯特丹大学）；Clue by BioWink GmbH（Clue by BioWink有限责任公司）；Stanford University（斯坦福大学）
High levels of missing data and strong class imbalance are ubiquitous challenges that are often presented simultaneously in real-world time series data. Existing methods approach these problems separately, frequently making significant assumptions about the underlying data generation process in order to lessen the impact of missing information. In this work, we instead demonstrate how a general self-supervised training method, namely Autoregressive Predictive Coding (APC), can be leveraged to overcome both missing data and class imbalance simultaneously without strong assumptions. Specifically…
A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification
KIOS Research and Innovation Center of Excellence（KIOS研究与创新中心）；Department of Electrical and Computer Engineering（电气与计算机工程系）；University of Cyprus（塞浦路斯大学）
Fast Convergence through Distributed Augmentation for Class-Imbalanced Federated Learning
Amar Nath and Shashi Khosla School of Information Technology, Indian Institute of Technology Delhi（印度理工学院德里分校 Amar Nath and Shashi Khosla 信息技术学院）；Department of Electrical Engineering, Indian Institute of Technology Delhi（印度理工学院德里分校电气工程系）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Free University of Berlin（柏林自由大学）；Leibniz University Hannover（莱布尼茨汉诺威大学）；L3S Research Center（L3S研究中心）
Recent studies showed that datasets used in fairness-aware machine learning for multiple protected attributes (referred to as multi-discrimination hereafter) are often imbalanced. The class-imbalance problem is more severe for the often underrepresented protected group (e.g. female, non-white, etc.) in the critical minority class. Still, existing methods focus only on the overall error-discrimination trade-off, ignoring the imbalance problem, thus amplifying the prevalent bias in the minority classes. Therefore, solutions are needed to solve the combined problem of multi-discrimination and cla…
We propose a highly data-efficient active learning framework for image classification. Our novel framework combines: (1) unsupervised representation learning of a Convolutional Neural Network and (2) the Gaussian Process (GP) method, in sequence to achieve highly data and label efficient classifications. Moreover, both elements are less sensitive to the prevalent and challenging class imbalance issue, thanks to the (1) feature learned without labels and (2) the Bayesian nature of GP. The GP-provided uncertainty estimates enable active learning by ranking samples based on the uncertainty and se…
Convolutional neural networks (CNNs) have achieved great success in skin lesion classification. A balanced dataset is required to train a good model. However, due to the appearance of different skin lesions in practice, severe or even deadliest skin lesion types (e.g., melanoma) naturally have quite small amount represented in a dataset. In that, classification performance degradation occurs widely, it is significantly important to have CNNs that work well on class imbalanced skin lesion image dataset. In this paper, we propose SuperCon, a two-stage training strategy to overcome the class imba…
Predicting Mortgage Default with Machine Learning: AutoML, Class Imbalance, and Leakage Control
Amazon Web Services, Inc.（亚马逊网络服务公司）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Connecticut（康涅狄格大学）；University at Buffalo, SUNY（布法罗大学）
KIOS Research and Innovation Center of Excellence（KIOS卓越研究与创新中心）；University of Cyprus（塞浦路斯大学）
We have witnessed in recent years an ever-growing volume of information becoming available in a streaming manner in various application areas. As a result, there is an emerging need for online learning methods that train predictive models on-the-fly. A series of open challenges, however, hinder their deployment in practice. These are, learning as data arrive in real-time one-by-one, learning from data with limited ground truth information, learning from nonstationary data, and learning from severely imbalanced data, while occupying a limited amount of memory for data storage. We propose the Ac…
From Data to Decision: A Multi-Stage Framework for Class Imbalance Mitigation in Optical Network Failure Analysis
Aston Institute of Photonic Technologies（阿斯顿光电技术研究所）；Aston University（阿斯顿大学）；Scuola Superiore Sant’Anna（圣安娜高等学院）；Nokia, Optical Networks（诺基亚光网络部门）；Instituto de Telecomunicações（电信研究所）；Instituto Superior Técnico（技术高级学院）
FedReLa: Imbalanced Federated Learning via Re-Labeling
School of Computing and Information Systems, University of Melbourne, Victoria, Australia（墨尔本大学计算机与信息系统学院）；School of Statistics and Data Science, LPMC, KLMDASR, and LEBPS, Nankai University（南开大学统计与数据科学学院）；School of Mathematics and Statistics, University of Melbourne, Victoria, Australia（墨尔本大学数学与统计学学院）
Beyond Rebalancing: Benchmarking Binary Classifiers Under Class Imbalance Without Rebalancing Techniques
Department of Information Systems and Security, College of Information Technology and Center for Artificial Intelligence and Digital Innovation, United Arab Emirates University（信息系统与安全系，信息技术学院和人工智能与数字创新中心，阿联酋大学）；College of Engineering and Technology, American University of the Middle East（工程与技术学院，中东大学）
Functional Random Forest with Adaptive Cost-Sensitive Splitting for Imbalanced Functional Data Classification
Fahad Mostafa School of Mathematical and Natural Sciences, Arizona State University, USA（法哈德·莫斯塔法 数学与自然科学学院，亚利桑那州立大学）；Hafiz Khan Department of Public Health, Julia Jones Matthews School of Population and Public Health, Texas Tech Health Sciences Center, USA（哈菲兹·汗 公共卫生部，朱莉亚·琼斯·马修斯流行病学与公共卫生学院，德克萨斯技术健康科学中心）
Hybrid Synthetic Data Generation with Domain Randomization Enables Zero-Shot Vision-Based Part Inspection Under Extreme Class Imbalance
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA；Materials \& Manufacturing Systems Research Lab, General Motors, Warren, MI 48092, USA
North China University of Science and Technology（华北理工大学）
The imbalanced classification problem turns out to be one of the important and challenging problems in data mining and machine learning. The performances of traditional classifiers will be severely affected by many data problems, such as class imbalanced problem, class overlap and noise. The Tomek-Link algorithm was only used to clean data when it was proposed. In recent years, there have been reports of combining Tomek-Link algorithm with sampling technique. The Tomek-Link sampling algorithm can effectively reduce the class overlap on data, remove the majority instances that are difficult to …
nCMD: Benign-Anchored Feature Selection for Imbalanced Network Intrusion Detection
Department of Computer Science（计算机科学系）；New Mexico State University（新墨西哥州立大学）；Graduate School of Informatics（信息科学研究生院）；Osaka Metropolitan University（大阪 Metropolitan 大学）
Class imbalance and noisy labels are the norm rather than the exception in many large-scale classification datasets. Nevertheless, most works in machine learning typically assume balanced and clean data. There have been some recent attempts to tackle, on one side, the problem of learning from noisy labels and, on the other side, learning from long-tailed data. Each group of methods make simplifying assumptions about the other. Due to this separation, the proposed solutions often underperform when both assumptions are violated. In this work, we present a simple two-stage approach based on recen…
