Class imbalance occurs when the class distribution is not equal. Namely, one class is under-
represented (minority class), and the other class has significantly more samples in the data …

Imbalanced classification problems are often encountered in many applications. The
challenge is that there is a minority class that has typically very little data and is often the …

Developing techniques for the machine learning of a classifier from class-imbalanced data
presents an important challenge. Among the existing methods for addressing this problem …

Abstract The Synthetic Minority Over-Sampling Technique (SMOTE) is one of the most well
known methods to solve the unequal class distribution problem in imbalanced datasets …

Traditional machine learning algorithms are always trapped by the class-imbalance problem
due to they are biased to the majority class. As one of the most efficient techniques to solve …

SMOTE (Synthetic Minority Oversampling TEchnique) is one of the most popular and well-
known sampling algorithms for addressing class imbalance learning problem. The merits of …

Imbalance learning is a challenging task for most standard machine learning algorithms.
The Synthetic Minority Oversampling Technique (SMOTE) is a well-known preprocessing …

Class imbalance learning is one of the most important topics in the field of machine learning
and data mining, and the Synthetic Minority Oversampling Techniques (SMOTE) is the …

Data imbalance is a common phenomenon in machine learning. In the imbalanced data
classification, minority samples are far less than majority samples, which makes it difficult for …

More often than not, data collected in real-time tends to be imbalanced ie, the samples
belonging to a particular class are significantly more than the others. This degrades the …