The imbalanced learning issue is related to the performance of learning algorithms in the
presence of asymmetrical class distribution. Due to the complex characteristics of …

Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

In severely imbalanced datasets, using traditional binary or multi-class classification typically
leads to bias towards the class (es) with the much larger number of instances. Under such …

As robots acquire increasingly sophisticated skills and see increasingly complex and varied
environments, the threat of an edge case or anomalous failure is ever present. For example …

In this study, we systematically investigate the impact of class imbalance on classification
performance of convolutional neural networks (CNNs) and compare frequently used …

In machine learning, if the training data is an unbiased sample of an underlying distribution,
then the learned classification function will make accurate predictions for new samples …

Data-driven condition monitoring reduces downtime of wind turbines and increases
reliability. Wind turbine operation and maintenance (O&M) cost is a significant factor that …

Despite more than two decades of continuous development learning from imbalanced data
is still a focus of intense research. Starting as a problem of skewed distributions of binary …

Novelty detection is the problem of identifying whether a new data point is considered to be
an inlier or an outlier. We assume that training data is available to describe only the inlier …

Many real-world data-mining applications involve obtaining predictive models using
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …