This paper proposes a new RWO-Sampling (Random Walk Over-Sampling) based on graphs for imbalanced datasets. In this method, two schemes based on under-sampling and over-sampling methods are introduced to keep the proximity information robust to noises and outliers. After constructing the first graph on minority class, RWO-Sampling will be implemented on selected samples, and the rest will remain unchanged. The second graph is constructed for the majority class, and the samples in a low-density area (outliers) are removed. Finally, in the proposed method, samples of the majority class in a high-density area are selected, and the rest are eliminated. Furthermore, utilizing RWO-sampling, the boundary of minority class is increased though the outliers are not raised. This method is tested, and the number of evaluation measures is compared to previous methods on nine continuous attribute datasets with different over-sampling rates and one data set for the diagnosis of COVID-19 disease. The experimental results indicated the high efficiency and flexibility of the proposed method for the classification of imbalanced data
In many machine learning domains, datasets are characterized by highly imbalanced and overlapping classes. Particularly in the medical domain, a specific list of symptoms can be labeled as one of various different conditions. Some of these conditions may be more prevalent than others by several orders of magnitude. Here we present a novel unsupervised domain adaptation scheme for such datasets. The scheme, based on a specific type of Quantification, is designed to work under both label and conditional shifts. It is demonstrated on datasets generated from electronic health records and provides …
Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event and the clinical decision of treatment planning. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features. In this paper, we propose a no…
Neural Architecture Search (NAS) provides state-of-the-art results when trained on well-curated datasets with annotated labels. However, annotating data or even having balanced number of samples can be a luxury for practitioners from different scientific fields, e.g., in the medical domain. To that end, we propose a NAS-based framework that bears the threefold contributions: (a) we focus on the self-supervised scenario, i.e., where no labels are required to determine the architecture, and (b) we assume the datasets are imbalanced, (c) we design each component to be able to run on a resource co…
Combining the increasing availability and abundance of healthcare data and the current advances in machine learning methods have created renewed opportunities to improve clinical decision support systems. However, in healthcare risk prediction applications, the proportion of cases with the condition (label) of interest is often very low relative to the available sample size. Though very prevalent in healthcare, such imbalanced classification settings are also common and challenging in many other scenarios. So motivated, we propose a variational disentanglement approach to semi-parametrically l…
Medical Image Classification on Imbalanced Data Using ProGAN and SMA-Optimized ResNet: Application to COVID-19
Department of Mechatronics, School of Intelligent Systems College of Interdisciplinary Science and Technology University of Tehran（塔里克大学机械工程系、智能系统学院跨学科科学与技术学院）；School of Science and Technology Faculty of Science, Agriculture, Business and Law University of New England（新英格兰大学科学与技术学院科学、农业、商业与法学院）
Recently, sound-based COVID-19 detection studies have shown great promise to achieve scalable and prompt digital pre-screening. However, there are still two unsolved issues hindering the practice. First, collected datasets for model training are often imbalanced, with a considerably smaller proportion of users tested positive, making it harder to learn representative and robust features. Second, deep learning models are generally overconfident in their predictions. Clinically, false predictions aggravate healthcare costs. Estimation of the uncertainty of screening would aid this. To handle the…
Edith Cowan University（伊迪丝·考恩大学）；University of Western Australia（西澳大学）
Annotating medical images for disease detection is often tedious and expensive. Moreover, the available training samples for a given task are generally scarce and imbalanced. These conditions are not conducive for learning effective deep neural models. Hence, it is common to 'transfer' neural networks trained on natural images to the medical image domain. However, this paradigm lacks in performance due to the large domain gap between the natural and medical image data. To address that, we propose a novel concept of Pre-text Representation Transfer (PRT). In contrast to the conventional transfe…
Indian Institute of Technology Delhi（印度理工学院德里分校）；Max Hospital（马克斯医院）；All India Institute of Medical Sciences（全印度医学科学研究所）
The gold standard for COVID-19 is RT-PCR, testing facilities for which are limited and not always optimally distributed. Test results are delayed, which impacts treatment. Expert radiologists, one of whom is a co-author, are able to diagnose COVID-19 positivity from Chest X-Rays (CXR) and CT scans, that can facilitate timely treatment. Such diagnosis is particularly valuable in locations lacking radiologists with sufficient expertise and familiarity with COVID-19 patients. This paper has two contributions. One, we analyse literature on CXR based COVID-19 diagnosis. We show that popular choices…
Commuting is an important part of daily life. With the gradual recovery from COVID-19 and more people returning to work from the office, the transmission of COVID-19 during commuting becomes a concern. Recent emerging mobility services (such as ride-hailing and bike-sharing) further deteriorate the infection risks due to shared vehicles or spaces during travel. Hence, it is important to quantify the infection risks in commuting. This paper proposes a probabilistic framework to estimate the risk of infection during an individual's commute considering different travel modes, including public tra…
School of Professional Studies（专业研究学院）；Northwestern University（西北大学）
Artificial intelligence (AI) is disrupting the medical field as advances in modern technology allow common household computers to learn anatomical and pathological features that distinguish between healthy and disease with the accuracy of highly specialized, trained physicians. Computer vision AI applications use medical imaging, such as lung chest X-Rays (LCXRs), to facilitate diagnoses by providing second-opinions in addition to a physician's or radiologist's interpretation. Considering the advent of the current Coronavirus disease (COVID-19) pandemic, LCXRs may provide rapid insights to ind…
Centre for Computational Intelligence (CCI), De Montfort University（德蒙福特大学计算智能中心）；Instituto Tecnologico de Costa Rica（哥斯达黎加理工学院）；School of Computer Science, University of Nottingham（诺丁汉大学计算机科学学院）；Imágenes Médicas Dr Chavarría Estrada（查瓦里亚·埃斯特拉达医学影像中心）；Biomedic Research Institute of Málaga (IBIMA)（马拉加生物医学研究所）
The Corona Virus (COVID-19) is an internationalpandemic that has quickly propagated throughout the world. The application of deep learning for image classification of chest X-ray images of Covid-19 patients, could become a novel pre-diagnostic detection methodology. However, deep learning architectures require large labelled datasets. This is often a limitation when the subject of research is relatively new as in the case of the virus outbreak, where dealing with small labelled datasets is a challenge. Moreover, in the context of a new highly infectious disease, the datasets are also highly im…
TCG Crest（TCG创新研究中心）；University College Dublin（都柏林大学学院）
In this work, we propose Random Walk-steered Majority Undersampling (RWMaU), which undersamples the majority points of a class imbalanced dataset, in order to balance the classes. Rather than marking the majority points which belong to the neighborhood of a few minority points, we are interested to perceive the closeness of the majority points to the minority class. Random walk, a powerful tool for perceiving the proximities of connected points in a graph, is used to identify the majority points which lie close to the minority class of a class-imbalanced dataset. The visit frequencies and the …
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…
Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院西奈山哈索·普拉特纳数字健康研究所）；Indiana University（印第安纳大学）；Weill Cornell Medicine（威尔康奈尔医学院）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Harvard University（哈佛大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
Machine Learning (ML) models typically require large-scale, balanced training data to be robust, generalizable, and effective in the context of healthcare. This has been a major issue for developing ML models for the coronavirus-disease 2019 (COVID-19) pandemic where data is highly imbalanced, particularly within electronic health records (EHR) research. Conventional approaches in ML use cross-entropy loss (CEL) that often suffers from poor margin classification. For the first time, we show that contrastive loss (CL) improves the performance of CEL especially for imbalanced EHR data and the re…
Norwegian University of Science and Technology（挪威科技大学）
While the whole world is still struggling with the COVID-19 pandemic, online learning and home office become more common. Many schools transfer their courses teaching to the online classroom. Therefore, it is significant to mine the students' feedback and opinions from their reviews towards studies so that both schools and teachers can know where they need to improve. This paper trains machine learning and deep learning models using both balanced and imbalanced datasets for sentiment classification. Two SOTA category-aware text generation GAN models: CatGAN and SentiGAN, are utilized to synthe…
University of Copenhagen（哥本哈根大学）；Copenhagen University Hospital（哥本哈根大学医院）
Cerebral Microbleeds (CMBs), typically captured as hypointensities from susceptibility-weighted imaging (SWI), are particularly important for the study of dementia, cerebrovascular disease, and normal aging. Recent studies on COVID-19 have shown an increase in CMBs of coronavirus cases. Automatic detection of CMBs is challenging due to the small size and amount of CMBs making the classes highly imbalanced, lack of publicly available annotated data, and similarity with CMB mimics such as calcifications, irons, and veins. Hence, the existing deep learning methods are mostly trained on very limit…
Institute of Information and Decision Technology, Dalian University of Technology（大连理工大学信息与决策技术研究所）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）
In this paper, we propose a deep residual network-based method, namely the DiCOVA-Net, to identify COVID-19 infected patients based on the acoustic recording of their coughs. Since there are far more healthy people than infected patients, this classification problem faces the challenge of imbalanced data. To improve the model's ability to recognize minority class (the infected patients), we introduce data augmentation and cost-sensitive methods into our model. Besides, considering the particularity of this task, we deploy some fine-tuning techniques to adjust the pre-training ResNet50. Further…
University of Assiut（艾斯尤特大学）；Birmingham City University（伯明翰城市大学）
Due to the high availability of large-scale annotated image datasets, knowledge transfer from pre-trained models showed outstanding performance in medical image classification. However, building a robust image classification model for datasets with data irregularity or imbalanced classes can be a very challenging task, especially in the medical imaging domain. In this paper, we propose a novel deep convolutional neural network, we called Self Supervised Super Sample Decomposition for Transfer learning (4S-DT) model. 4S-DT encourages a coarse-to-fine transfer learning from large-scale image rec…
University of Chinese Academy of Sciences（中国科学院大学）；Ant Financial（蚂蚁金融）
In network embedding, random walks play a fundamental role in preserving network structures. However, random walk based embedding methods have two limitations. First, random walk methods are fragile when the sampling frequency or the number of node sequences changes. Second, in disequilibrium networks such as highly biases networks, random walk methods often perform poorly due to the lack of global network information. In order to solve the limitations, we propose in this paper a network diffusion based embedding method. To solve the first limitation, our method employs a diffusion driven proc…
Graph embedding, representing local and global neighborhood information by numerical vectors, is a crucial part of the mathematical modeling of a wide range of real-world systems. Among the embedding algorithms, random walk-based algorithms have proven to be very successful. These algorithms collect information by creating numerous random walks with a redefined number of steps. Creating random walks is the most demanding part of the embedding process. The computation demand increases with the size of the network. Moreover, for real-world networks, considering all nodes on the same footing, the…
The Gait Signature of Frailty: Transfer Learning based Deep Gait Models for Scalable Frailty Assessment
Department of Electrical and Computer Engineering（电气工程系）；Johns Hopkins University（约翰霍普金斯大学）；Division of Geriatrics and Gerontology Medicine（老年医学科）；Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院）；Department of Computer Science（计算机科学系）；Center for Movement Studies（运动研究中心）；Kennedy Krieger Institute（肯尼迪-克里格尔研究所）；Department of Physical Medicine and Rehabilitation（康复医学系）；Department of Biomedical Engineering（生物医学工程系）
College of Computer and Information Science, Southwest University（西南大学计算机与信息科学学院）
Heterogeneous information network (HIN) embedding aims to find the representations of nodes that preserve the proximity between entities of different nature. A family of approaches that are wildly adopted applies random walk to generate a sequence of heterogeneous context, from which the embedding is learned. However, due to the multipartite graph structure of HIN, hub nodes tend to be over-represented in the sampled sequence, giving rise to imbalanced samples of the network. Here we propose a new embedding method CoarSAS2hvec. The self-avoid short sequence sampling with the HIN coarsening pro…
Machine Learning and the Random Walk Puzzle: Forecasting the CAD/USD Exchange Rate with Expanding Window Evaluation and SHAP Interpretability
Department of Economics, University of Ottawa（Ottawa大学经济学系）；Department of Biostatistics and Data Science, Celia Scott Weatherhead School of Public Health and Tropical Medicine at Tulane University（Tulane大学生物统计学与数据科学系）；Department of Statistics, Western Michigan University（西方密苏里大学统计学系）；Department of Economics, Western Michigan University（西方密苏里大学经济学系）；School of Mathematical and Statistical Sciences, University of Texas Rio Grande Valley（德克萨斯里奥格兰德谷大学数学与统计学系）；Robinson College of Business, Georgia State University（佐治亚州立大学罗宾逊商学院）；Department of Mathematics & Statistics, University of North Florida（北佛罗里达大学数学与统计学系）；Department of Epidemiology and Biostatistics, University of Arizona（亚利桑那大学流行病学与生物统计学系）；Department of Statistics and Actuarial Science, University of Ghana（加纳大学统计学与精算科学系）
Prediction with Expert Advice under Local Differential Privacy
Department of Computer Sciences University of Wisconsin — Madison（计算机科学系威斯康星大学麦迪逊分校）；Department of Electrical and Computer Engineering University of Wisconsin — Madison（电气与计算机工程系威斯康星大学麦迪逊分校）
