Imbalanced data set, a problem often found in real world application, can cause seriously negative effect on classification performance of machine learning algorithms. There have been many attempts at dealing with classification of unbalanced data sets. To handle the problem of imbalanced data is to re balance them artificially by oversampling and/or under-sampling.

As growing numbers of real world applications involve imbalanced class distribution or unequal costs for misclassification errors in different classes, learning from imbalanced class distribution is considered to be one of the most challenging issues in data mining research. This study empirically investigates the sensitivity of bagging predictors with respect to 12 algorithms and 9 levels of c...

In Machine Learning, a data set is imbalanced when the class proportions are highly skewed. Imbalanced data sets arise routinely in many application domains and pose a challenge to traditional classifiers. We propose a new approach to building ensembles of classifiers for two-class imbalanced data sets, called Random Balance. Each member of the Random Balance ensemble is trained with data sampl...

Solving optimization models (including parameters fitting) for support vector machines on largescale training data is often an expensive computational task. This paper proposes a multilevel algorithmic framework that scales efficiently to very large data sets. Instead of solving the whole training set in one optimization process, the support vectors are obtained and gradually refined at multipl...

When performing a classification task, we may find some data-sets with a different class distribution among their patterns. This problem is known as classification with imbalanced data-sets and it appears in many real application areas. For this reason, it has recently become a relevant topic in the area of Machine Learning. The aim of this work is to improve the behaviour of fuzzy rule based c...

BACKGROUND In a medical data set, data are commonly composed of a minority (positive or abnormal) group and a majority (negative or normal) group and the cost of misclassifying a minority sample as a majority sample is highly expensive. This is the so-called imbalanced classification problem. The traditional classification functions can be seriously affected by the skewed class distribution in ...