Magnetic resonance imaging (MRI) is known to be a slow imaging modality and undersampling in k-space has been used to increase the imaging speed. However, image reconstruction from undersampled k-space data is an ill-posed inverse problem. Iterative algorithms based on compressed sensing have been used to address the i…
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48 results for “over/undersampling”
Proposes a new undersampling method for imbalanced data classification.
problem Challenges of oversampling and undersampling in imbalanced data.
method Bilevel optimization framework for identifying optimal subset of majority training data.
result Improves F1 scores by up to 10% compared to state-of-the-art methods.
A novel two-stage resampling method improves CNN training on imbalanced colorectal cancer image data.
problem Data imbalance in medical image datasets, especially in histopathological images.
method Two-stage resampling: first oversampling in image space, then undersampling in feature space.
result The proposed method enhances CNN training on imbalanced colorectal cancer image datasets.
Two new undersampling methods improve classification accuracy for imbalanced datasets.
problem Class imbalance and distributional differences in large datasets lead to biased models and poor predictive performance.
method Mutual information-based stratified simple random sampling and support points optimization.
result Empirical results show higher balanced classification accuracy compared to traditional techniques.
Undersampling often outperforms other methods in nonparametric classification.
problem Distribution shift challenges in nonparametric binary classification.
method Proved undersampling is minimax optimal in worst-case scenarios.
result Undersampling is a robustness intervention with theoretical guarantees.
Proposes a new technique for handling imbalanced data.
problem Data imbalance in classification tasks.
method Combines oversampling and undersampling techniques.
result CSMOUTE shows promise for handling imbalanced datasets.
The problem of class imbalance along with class-overlapping has become a major issue in the domain of supervised learning. Most supervised learning algorithms assume equal cardinality of the classes under consideration while optimizing the cost function and this assumption does not hold true for imbalanced datasets whi…
Class imbalance problem is commonly faced while developing machine learning models for real-life issues. Due to this problem, the fitted model tends to be biased towards the majority class data, which leads to lower precision, recall, AUC, F1, G-mean score. Several researches have been done to tackle this problem, most…
Jointly correct bias fields and reconstruct undersampled MRI images.
problem Recovering fully sampled MRI images from undersampled data while accounting for bias field differences.
method An unsupervised learning-based reconstruction algorithm combined with a N4-based bias field estimation method in a joint optimization scheme.
result The proposed method improves reconstruction quality, both visually and in terms of RMSE.
Study uses 1D-CNNs to forecast mortality in ELSA survey.
problem Forecasting mortality in the middle-aged and older population.
method 1D-CNNs applied to longitudinal data with various over/undersampling and activation functions.
result Swish nonlinearity outperforms other functions in forecasting mortality.
Unified framework for imbalanced data resampling improves classification performance.
problem Data imbalance negatively impacts machine learning performance.
method Unified framework combining over- and undersampling with radial basis functions optimization.
result Potential Anchoring outperforms state-of-the-art resampling algorithms.
This study proposes a method to predict ICU infections from imbalanced data using clustering-based undersampling and ensemble classifiers.
problem Predicting healthcare-associated infections in ICU patients from imbalanced data.
method Clustering-based undersampling strategy combined with ensemble classifiers.
result The proposed method outperforms other resampling techniques in predicting ICU infections.
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 numbe…
We aim at developing and improving the imbalanced business risk modeling via jointly using proper evaluation criteria, resampling, cross-validation, classifier regularization, and ensembling techniques. Area Under the Receiver Operating Characteristic Curve (AUC of ROC) is used for model comparison based on 10-fold cro…
We introduce a two step algorithm with theoretical guarantees to recover a jointly sparse and low-rank matrix from undersampled measurements of its columns. The algorithm first estimates the row subspace of the matrix using a set of common measurements of the columns. In the second step, the subspace aware recovery of …
In many applications one may acquire a composition of several signals that may be corrupted by noise, and it is a challenging problem to reliably separate the components from one another without sacrificing significant details. Adding to the challenge, in a compressive sensing framework, one is given only an undersampl…
Algorithms for Magnetic Resonance (MR) image reconstruction from undersampled measurements exploit prior information to compensate for missing k-space data. Deep learning (DL) provides a powerful framework for extracting such information from existing image datasets, through learning, and then using it for reconstructi…
Better Together: Cross and Joint Covariances Enhance Signal Detectability in Undersampled Datacond-mat.dis-nn
Better signal detection in undersampled data using joint and cross covariances.
problem Detecting shared signals in high-dimensional data with limited samples.
method Analysis of three covariance matrices: individual, cross, and joint.
result Joint and cross covariance matrices detect signals earlier than individual covariances.
Generative adversarial networks reconstruct MRI images without full data.
problem Lack of fully-sampled ground truth data for supervised MRI reconstruction.
method Generative adversarial networks for unsupervised MRI reconstruction.
result Reconstructed images show more anatomical structure than conventional methods.
Natural signals and images are well-known to be approximately sparse in transform domains such as Wavelets and DCT. This property has been heavily exploited in various applications in image processing and medical imaging. Compressed sensing exploits the sparsity of images or image patches in a transform domain or synth…
Machine learning models are widely adopted in scenarios that directly affect people. The development of software systems based on these models raises societal and legal concerns, as their decisions may lead to the unfair treatment of individuals based on attributes like race or gender. Data preparation is key in any ma…
Subspace learning and matrix factorization problems have great many applications in science and engineering, and efficient algorithms are critical as dataset sizes continue to grow. Many relevant problem formulations are non-convex, and in a variety of contexts it has been observed that solving the non-convex problem d…
We propose to use Gaussian process regression to accurately estimate the diffusion MRI signal at arbitrary locations in q-space. By estimating the signal on a grid, we can do synthetic diffusion spectrum imaging: reconstructing the ensemble averaged propagator (EAP) by an inverse Fourier transform. We also propose an a…
Extremely preterm infants often require endotracheal intubation and mechanical ventilation during the first days of life. Due to the detrimental effects of prolonged invasive mechanical ventilation (IMV), clinicians aim to extubate infants as soon as they deem them ready. Unfortunately, existing strategies for predicti…
Study proposes new methods to calculate probabilistic benchmarks in noisy data.
problem Identifying opportunities for improvement in comparable units with noisy data.
method 2-step methodology involving undersampling and relevance vector machine.
result Higher discrimination power achieved with macro-economic environment variables.
In "extreme" computational imaging that collects extremely undersampled or noisy measurements, obtaining an accurate image within a reasonable computing time is challenging. Incorporating image mapping convolutional neural networks (CNN) into iterative image recovery has great potential to resolve this issue. This pape…
Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learningcs.LG
Finding parameters that minimise a loss function is at the core of many machine learning methods. The Stochastic Gradient Descent algorithm is widely used and delivers state of the art results for many problems. Nonetheless, Stochastic Gradient Descent typically cannot find the global minimum, thus its empirical effect…
In this paper we develop a novel computational sensing framework for sensing and recovering structured signals. When trained on a set of representative signals, our framework learns to take undersampled measurements and recover signals from them using a deep convolutional neural network. In other words, it learns a tra…
HardVis helps visually manage imbalanced data by sampling hard instances.
problem Managing unsafe or noisy instances in imbalanced classification.
method Visual analytics system using undersampling and oversampling techniques.
result Boosts predictive power of ML models by balancing data sets.
This chapter tackles class imbalance in datasets to promote data democracy.
problem Class imbalance in datasets leading to biased decisions and policies.
method Statistical measures and data-level methods (oversampling, undersampling, etc.) applied to a real dataset.
result Popular data-level methods improve performance in handling class imbalance.
High-dimensional data are ubiquitous in contemporary science and finding methods to compress them is one of the primary goals of machine learning. Given a dataset lying in a high-dimensional space (in principle hundreds to several thousands of dimensions), it is often useful to project it onto a lower-dimensional manif…
In linear inverse problems, the goal is to recover a target signal from undersampled, incomplete or noisy linear measurements. Typically, the recovery relies on complex numerical optimization methods; recent approaches perform an unfolding of a numerical algorithm into a neural network form, resulting in a substantial …
This paper explores deep learning for improving X-ray CT image reconstruction from undersampled data.
problem Improving image reconstruction from undersampled X-ray CT data.
method Analysis of classical and deep learning methods for solving inverse problems.
result Deep learning methods show promise in improving image quality from undersampled data.
A new method detects hallucinations in medical image restoration using Fourier Ring Correlation.
problem Detecting hallucinations in DL outputs for medical image restoration.
method sFRC (s(c)can FRC) over small patches of DL outputs and reference images.
result sFRC effectively detects hallucinations in CT and MRI restoration problems.
Proposes a Monte-Carlo method for sparse signal reconstruction.
problem Reconstructing sparse signals in high-dimensional settings.
method Greedy Monte-Carlo (GMC) search algorithm.
result GMC can achieve perfect reconstruction in undersampling situations.
Machine learning has automated much of financial fraud detection, notifying firms of, or even blocking, questionable transactions instantly. However, data imbalance starves traditionally trained models of the content necessary to detect fraud. This study examines three separate factors of credit card fraud detection vi…
We use a geometric digraph family called class cover catch digraphs (CCCDs) to tackle the class imbalance problem in statistical classification. CCCDs provide graph theoretic solutions to the class cover problem and have been employed in classification. We assess the classification performance of CCCD classifiers by ex…
Compressed sensing is a powerful tool in applications such as magnetic resonance imaging (MRI). It enables accurate recovery of images from highly undersampled measurements by exploiting the sparsity of the images or image patches in a transform domain or dictionary. In this work, we focus on blind compressed sensing (…
Estimating the covariance structure of multivariate time series is a fundamental problem with a wide-range of real-world applications -- from financial modeling to fMRI analysis. Despite significant recent advances, current state-of-the-art methods are still severely limited in terms of scalability, and do not work wel…
Fast and accurate MRI image reconstruction from undersampled data is crucial in clinical practice. Deep learning based reconstruction methods have shown promising advances in recent years. However, recovering fine details from undersampled data is still challenging. In this paper, we introduce a novel deep learning bas…
BSAC improves credit scoring models by leveraging autoencoders and addressing imbalanced datasets.
problem Imbalanced and heterogeneous credit scoring datasets.
method Bagging Supervised Autoencoder Classifier (BSAC) that uses autoencoders and undersampling.
result BSAC improves classification of loan applicants, demonstrating robustness and effectiveness.
A new method flips class values to address class and treatment imbalance in uplift modeling and HTE.
problem Class and treatment imbalance in imbalanced RCT data.
method Class flipping approach to address imbalance without distorting predictions.
result The method does not distort predicted effects and does not require calibration.
This paper surveys various data balancing methods for imbalanced datasets.
problem Imbalanced datasets bias predictions and degrade classifier performance.
method Extensive review of oversampling, undersampling, adaptive, generative, combination, and ensemble methods.
result No single method universally outperforms others; selection depends on dataset characteristics.
Develops a method for identifying structured dynamical systems from data.
problem Identifying structured dynamical systems from undersampled and noisy data.
method Sparse least-squares fitting via optimization with the alternating direction method of multipliers.
result The method is stable and successful under certain conditions, as shown by theoretical guarantees and computational results.
In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue. Class imbalance is a common problem that has been comprehensively studied in classical machine learning, yet very lim…
We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: it is possible to learn to restore images by only looking at corrupted examples, at performance at and sometimes exceeding training using…
Sparsity-based approaches have been popular in many applications in image processing and imaging. Compressed sensing exploits the sparsity of images in a transform domain or dictionary to improve image recovery from undersampled measurements. In the context of inverse problems in dynamic imaging, recent research has de…
Recent sparse MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling much faster MRI scanning. However, these techniques sometimes struggle to reconstruct sharp images that preserve fine detail while maintaining a nat…