Classification of imbalanced data has drawn significant attention from research community in last decade. As the distribution of data into various classes affects the performances of traditional classifiers, the imbalanced data needs special treatment. Modification in learning approaches is one of the solutions to deal with such cases. In this paper a hybrid nearest neighbor learning approach i...

Some real world data mining applications present imbalanced or skewed class distributions. In these domains, the underrepresented classes are often the ones we are more interested in. However, most learning algorithms are not able to induce meaningful classifiers in some imbalanced domains. One reason for this poor performance is that learning algorithms tend to focus in abundant classes to max...

Recently, Class imbalance problems have growing interest because of their classification difficulty caused by the imbalanced class distributions. In particular, many ensemble learning and machine learning methods have been proposed for classification of imbalance problem. However, these methods producing poor predictive accuracy of classification for two-class imbalanced dataset. In this paper,...

There are real-world dataset where we can found classes with a very different percentage of patterns between them, that is to say we have classes represented by many examples (high percentage of patterns) and classes represented by few examples (low percentage of patterns). These kind of datasets receive the name of “imbalanced datasets”. In the field of classification problems the imbalanced d...

This paper presents a data mining application in metabolomics. It aims at building an enhanced machine learning classifier that can be used for diagnosing cachexia syndrome and identifying its involved biomarkers. To achieve this goal, a data-driven analysis is carried out using a public dataset consisting of 1H-NMR metabolite profile. This dataset suffers from the problem of imbalanced classes...

In many real classification problems the data are imbalanced, i.e., the number of instances for some classes are much higher than that of the other classes. Solving a classification task using such an imbalanced data-set is difficult due to the bias of the training towards the majority classes. The aim of this contribution is to analyse the performance of CORBFN, a cooperative-competitive evolu...

Learning from imbalanced data, where the number of observations in one class is significantly rarer than in other classes, has gained considerable attention in the data mining community. Most existing literature focuses on binary imbalanced case while multi-class imbalanced learning is barely mentioned. What’s more, most proposed algorithms treated all imbalanced data consistently and aimed to ...

Classification of data is difficult if the data is imbalanced and classes are overlapping. In recent years, more research has started to focus on classification of imbalanced data since real world data is often skewed. Traditional methods are more successful with classifying the class that has the most samples (majority class) compared to the other classes (minority classes). For the classifica...

Image classification datasets are often imbalanced, characteristic that negatively affects the accuracy of deeplearning classifiers. In this work we propose balancing GANs (BAGANs) as an augmentation tool to restore balance in imbalanced datasets. This is challenging because the few minority-class images may not be enough to train a GAN. We overcome this issue by including during training all a...

Imbalanced data creates two problems for machine learning. First, even if the training set is large, the sample size of smaller classes may be small. Learning accurate models from small samples is hard. Multitask learning is one way to learn more accurate models from small samples that is particularly well suited to imbalanced data. A second problem when learning from imbalanced ata is that the...