Many real-world data-mining applications involve obtaining predictive models using
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …

Learning classifiers from imbalanced or skewed datasets is an important topic, arising very
often in practice in classification problems. In such problems, almost all the instances are …

Class imbalance is a common problem in the case of real-world object detection and
classification tasks. Data of some classes are abundant, making them an overrepresented …

In recent years, mining with imbalanced data sets receives more and more attentions in both
theoretical and practical aspects. This paper introduces the importance of imbalanced data …

Undersampling is a popular method in dealing with class-imbalance problems, which uses
only a subset of the majority class and thus is very efficient. The main deficiency is that many …

The class imbalance problem has been recognized in many practical domains and a hot
topic of machine learning in recent years. In such a problem, almost all the examples are …

The class imbalance problem is encountered in real-world applications of machine learning
and results in a classifier's suboptimal performance. Researchers have rigorously studied …

The class imbalance problem is encountered in a large number of practical applications of
machine learning and data mining, for example, information retrieval and filtering, and the …

This paper deals with the application of support vector machine (SVM) to deal with the class
imbalance problem. The objective of this paper is to examine the feasibility and efficiency of …

This research develops a MapReduce framework for automatic pattern recognition based on
fault diagnosis by solving data imbalance problem in a cloud-based manufacturing (CBM) …