Cross-validation is a mainstay for measuring performance and progress in machine
learning. There are subtle differences in how exactly to compute accuracy, F-measure and …

Many real-world classification problems are significantly class-imbalanced to detriment of
the class of interest. The standard set of proper evaluation metrics is well-known but the …

Abstract Machine learning algorithms have successfully entered industry through many real-
world applications (eg, search engines and product recommendations). In these …

Common arguments for including applications papers in the Machine Learning literature are
often based on the papers' value for advertising success stories and for morale boosting. For …

It is often assumed that class imbalances are responsible for significant losses of
performance in standard classifiers. The purpose of this paper is to the question whether …

Practitioners of data mining and machine learning have long observed that the imbalance of
classes in a data set negatively impacts the quality of classifiers trained on that data …

This paper gives an overview of some ways in which our understanding of performance
evaluation measures for machine-learned classifiers has improved over the last twenty …

This paper takes a new look at two sampling schemes commonly used to adapt machine
learning algorithms to imbalanced classes and misclassification costs. It uses a performance …

This study discusses the effects of class imbalance and training data size on the predictive
performance of classifiers. An empirical study was performed on ten classifiers arising from …

Cross-validation is a very commonly employed technique used to evaluate classifier
performance. However, it can potentially introduce dataset shift, a harmful factor that is often …