Since canonical machine learning algorithms assume that the dataset has equal number of
samples in each class, binary classification became a very challenging task to discriminate …

Class imbalance (CI) is a well-known problem in data science. Nowadays, it is affecting the
data modeling of many of the real-world processes that are being digitized. The …

To predict the class level of any classification problem, predictive models are used and
mostly a single predictive model is built to predict the class level of any classification …

随着信息技术的快速发展, 各领域的数据正以前所未有的速度产生并被广泛收集和存储,
如何实现数据的智能化处理从而利用数据中蕴含的有价值信息已成为理论和应用的研究热点 …

Context: In practice, software datasets tend to have more non-defective instances than
defective ones, which is referred to as the class imbalance problem in software defect …

Imbalanced data classification poses a major challenge in data mining community. Although
standard support vector machine can generally show relatively robust performance in …

Background Imbalance between positive and negative outcomes, a so-called class
imbalance, is a problem generally found in medical data. Despite various studies, class …

Today, classification of imbalanced datasets, in which the samples belonging to one class is
more than the samples pertaining to other classes, has been paid much attention owing to …

In this paper, we focus on data-driven approaches to human activity recognition (HAR). Data-
driven approaches rely on good quality data during training, however, a shortage of high …

Context: Classification of software requirements into different categories is a critically
important task in requirements engineering (RE). Developing machine learning (ML) …