Few-Shot Learning (FSL) algorithms are commonly trained through Meta-Learning (ML), which exposes models to batches of tasks sampled from a meta-dataset mimic seen during evaluation. However, the standard training procedures overlook real-world dynamics where classes occur at different frequencies. While it is generally understood that class imbalance harms performance supervised methods, limit...

Anomalies are rare events. For anomaly detection, severe class imbalance is the norm. Although there has been much research into imbalanced classes, there are surprisingly few examples of dealing with severe imbalance. Alternative performance measures have superseded error rate, or accuracy, for algorithm comparison. But whatever their other merits, they tend to obscure the severe imbalance pro...

In practice, pattern recognition applications often suffer from imbalanced data distributions between classes, which may vary during operations w.r.t. the design data. Two-class classification systems designed using imbalanced data tend to recognize the majority (negative) class better, while the class of interest (positive class) often has the smaller number of samples. Several data-level tech...

Streaming data is pervasive in a multitude of data mining applications. One fundamental problem in the task of mining streaming data is distributional drift over time. Streams may also exhibit high and varying degrees of class imbalance, which can further complicate the task. In scenarios like these, class imbalance is particularly difficult to overcome and has not been as thoroughly studied. I...

The rich history of predictive modeling has culminated in a diverse set of techniques capable of making accurate predictions on many real-world problems. Many of these techniques demand training, whereby a set of instances with ground-truth labels (values of a dependent variable) are observed by a model-building process that attempts to capture, at least in part, the relationship between the fe...

Federated learning (FL) is a promising approach for training decentralized data located on local client devices while improving efficiency and privacy. However, the distribution quantity of clients' side may lead to significant challenges such as class imbalance non-IID (non-independent identically distributed) data, which could greatly impact performance common model. While much effort has bee...

Class imbalance is one of the challenging problems for machine learning in many real-world applications. Many methods have been proposed to address and attempt to solve the problem, including sampling and cost-sensitive learning. The latter has attracted significant attention in recent years to solve the problem, but it is difficult to determine the precise misclassification costs in practice. ...

Abstract Many real-life datasets are imbalanced in nature, which implies that the number of samples present one class (minority class) is exceptionally less compared to found other (majority class). Hence, if we directly fit these a standard classifier for training, then it often overlooks minority while estimating separating hyperplane(s) and as result missclassifies samples. To solve this pro...