Current important challenges in data mining research are triggered by the need to address
various particularities of real-world problems, such as imbalanced data and error cost …

Class imbalance is a common problem encountered in applying machine learning tools to
real-world data. It causes most classifiers to perform sub-optimally and yield very poor …

Imbalanced dataset is an important factor influencing the effect of learning algorithms. Its
influence on the classification learner is even more universal. To deal with imbalanced …

Classification predictive modeling involves the accurate assignment of observations in a
dataset to target classes or categories. Real-world classification problems with severely …

Classification of imbalanced data is a challenging task in machine learning, as most
classification approaches tend to bias towards the majority class, even though the minority …

Highly imbalanced datasets continue to be a challenge in many data mining applications. It
is surprising that state-of-the-art techniques countering class imbalances are usually very …

This paper describes a new learning strategy on the problem of classification on overlapped
and imbalanced training set. We devise an adaptive scheme for minority generating; with …

Approaches to imbalanced classification problem usually focus on rebalancing the class
sizes, neglecting the effect of hidden structure within the majority class. The aim of this paper …

The class imbalance problem (when one of the classes has much less samples than the
others) is of great importance in machine learning, because it corresponds to many critical …

The biased minimax probability machine (BMPM) constructs a classifier which deals with the
imbalanced learning tasks. It provides a worst-case bound on the probability of …