In a majority–minority classification problem, class imbalance in the dataset (s) can
dramatically skew the performance of classifiers, introducing a prediction bias for the …

The massive growth in the scale of data has been observed in recent years being a key
factor of the Big Data scenario. Big Data can be defined as high volume, velocity and variety …

While the recent emergence of a large number of online course resources has made life
more convenient for many people, it has also caused information overload. According to a …

Severe class imbalance between majority and minority classes in Big Data can bias the
predictive performance of Machine Learning algorithms toward the majority (negative) class …

Big Data applications are emerging during the last years, and researchers from many
disciplines are aware of the high advantages related to the knowledge extraction from this …

The class imbalance problem has become a leading challenge. Although conventional
imbalance learning methods are proposed to tackle this problem, they have some …

Imbalanced problems can arise in different real-world situations, and to address this, certain
strategies in the form of resampling or balancing algorithms are proposed. This issue has …

Class imbalance is an important problem in machine learning. With increases in available
information and the growing use of Big Data sources to extract meaning from data, the …

Data has become much widely available in recent years. Since the past years, Learning
classifiers from unbalanced data is a crucial issue that comes up frequently in classification …

This paper aims to address a key research issue regarding the ECBDL'14 bioinformatics big
data competition. The ECBDL'14 dataset was the big data target in the competition, and it …