P Cao, D Zhao, O Zaiane - … -Asia conference on knowledge discovery and …, 2013 - Springer
Class imbalance is one of the challenging problems for machine learning in many real-world applications. Cost-sensitive learning has attracted significant attention in recent years to …
C Drummond, RC Holte - … on learning from imbalanced datasets II, 2003 - eiti.uottawa.ca
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 …
Traditional classification algorithms can be limited in their performance on highly unbalanced data sets. A popular stream of work for countering the problem of class …
Cost-sensitive learning is an aspect of algorithm-level modifications for class imbalance. Here, instead of using a standard error-driven evaluation (or 0–1 loss function), a …
Classifier learning with datasets which suffer from imbalanced class distributions is an important problem in data mining. This issue occurs when the number of examples …
Z Sun, Q Song, X Zhu, H Sun, B Xu, Y Zhou - Pattern Recognition, 2015 - Elsevier
The class imbalance problems have been reported to severely hinder classification performance of many standard learning algorithms, and have attracted a great deal of …
P Kaur, A Gosain - ICT Based Innovations: Proceedings of CSI 2015, 2018 - Springer
Class imbalance learning is a recent topic, which helps us to detect the classes from unbalanced datasets. In various real scenarios, where we need to find the exceptional cases …
This chapter is concerned with the class imbalance problem, which has been recognized as a crucial problem in machine learning and data mining. The problem occurs when there are …
Class imbalance is among the most persistent complications which may confront the traditional supervised learning task in real-world applications. The problem occurs, in the …