The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …

Data preprocessing and reduction have become essential techniques in current knowledge
discovery scenarios, dominated by increasingly large datasets. These methods aim at …

Learning and mining from imbalanced datasets gained increased interest in recent years.
One simple but efficient way to increase the performance of standard machine learning …

Classification datasets often have an unequal class distribution among their examples. This
problem is known as imbalanced classification. The Synthetic Minority Over-sampling …

Imbalanced classification is an important task in supervised learning, and Synthetic Minority
Over-sampling Technique (SMOTE) is the most common method to address it. However, the …

Classification with big data has become one of the latest trends when talking about learning
from the available information. The data growth in the last years has rocketed the interest in …

Classification problem for imbalanced datasets is pervasive in a lot of data mining domains.
Imbalanced classification has been a hot topic in the academic community. From data level …

Unbalanced classification is an essential machine learning task, which has attracted
widespread attention from both the academic and industrial communities due mainly to its …

An oversampling technique balances a dataset by increasing the number of minority
samples. It is a common and effective method in imbalanced learning. However, most …

The class imbalance problem is a challenge in supervised classification, since many
classifiers are sensitive to class distribution, biasing their prediction towards the majority …