A major issue in the classification of class imbalanced datasets involves the determination of
the most suitable performance metrics to be used. In previous work using several examples …

Class imbalance in a dataset is a major problem for classifiers that results in poor prediction
with a high true positive rate (TPR) but a low true negative rate (TNR) for a majority positive …

Abstract Machine learning methods require a vast amount of data to train a model. The data
necessary for landslide susceptibility mapping is a collection of landslide causative factors …

Indices quantifying the performance of classifiers under class-imbalance, often suffer from
distortions depending on the constitution of the test set or the class-specific classification …

We propose a novel system leveraging deep learning-based methods to predict urban traffic
accidents and estimate their severity. The major challenge is the data imbalance problem in …

<? show [AQ ID= GQ2 POS=-24pt]?><? show [AQ ID= GQ5 POS= 12pt]?> This paper
proposes a solution to the problem of automatically predicting hysteresis and backbone …

Traditional classification tasks suffer from the class-imbalanced problem, where some
classes far outnumber others. To address this issue, existing class-imbalanced learning …

Thaw slump susceptibility mapping (TSSM) of Qinghai–Tibet railway corridor (QTRC) is the
prerequisite and basis for disaster assessment and prevention of permafrost projects. The …

Support vector machines (SVMs) are well-known machine learning algorithms for
classification and regression applications. In the healthcare domain, they have been used …

To improve the classification performance of support vector machines (SVMs) on
imbalanced datasets, cost-sensitive learning methods have been proposed, eg, Different …