Learning from class-imbalanced data: Review of methods and applications
Rare events, especially those that could potentially negatively impact society, often require
humans' decision-making responses. Detecting rare events can be viewed as a prediction …
humans' decision-making responses. Detecting rare events can be viewed as a prediction …
A survey of predictive modeling on imbalanced domains
Many real-world data-mining applications involve obtaining predictive models using
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
Background To evaluate binary classifications and their confusion matrices, scientific
researchers can employ several statistical rates, accordingly to the goal of the experiment …
researchers can employ several statistical rates, accordingly to the goal of the experiment …
Modeling the distribution of normal data in pre-trained deep features for anomaly detection
Anomaly Detection (AD) in images is a fundamental computer vision problem and refers to
identifying images and/or image substructures that deviate significantly from the norm …
identifying images and/or image substructures that deviate significantly from the norm …
Convolutional neural networks for large-scale remote-sensing image classification
We propose an end-to-end framework for the dense, pixelwise classification of satellite
imagery with convolutional neural networks (CNNs). In our framework, CNNs are directly …
imagery with convolutional neural networks (CNNs). In our framework, CNNs are directly …
Siamese transformer network for hyperspectral image target detection
Hyperspectral target detection can be described as locating targets of interest within a
hyperspectral image based on prior information of targets. The complexity of actual scenes …
hyperspectral image based on prior information of targets. The complexity of actual scenes …
Hyperspectral anomaly detection with attribute and edge-preserving filters
A novel method for anomaly detection in hyperspectral images is proposed. The method is
based on two ideas. First, compared with the surrounding background, objects with …
based on two ideas. First, compared with the surrounding background, objects with …
Hyperspectral anomaly detection based on machine learning: An overview
Hyperspectral anomaly detection (HAD) is an important hyperspectral image application.
HAD can find pixels with anomalous spectral signatures compared with their neighbor …
HAD can find pixels with anomalous spectral signatures compared with their neighbor …
COVID-19 diagnosis by routine blood tests using machine learning
Physicians taking care of patients with COVID-19 have described different changes in
routine blood parameters. However, these changes hinder them from performing COVID-19 …
routine blood parameters. However, these changes hinder them from performing COVID-19 …
Discriminative reconstruction constrained generative adversarial network for hyperspectral anomaly detection
The rich and distinguishable spectral information in hyperspectral images (HSIs) makes it
possible to capture anomalous samples [ie, anomaly detection (AD)] that deviate from …
possible to capture anomalous samples [ie, anomaly detection (AD)] that deviate from …