A dataset is imbalanced if the classification categories are not approximately equally represented. Recent years brought increased interest in applying machine learning techniques to difficult "real-world" problems, many of which are characterized by imbalanced data. Additionally the distribution of the testing data may differ from that of the training data, and the true misclassification costs...

Clinical datasets commonly have an imbalanced class distribution and high dimensional variables. Imbalanced class means that one class is represented by a large number (majority) of samples more than another (minority) one in binary classification [1]. For example, in our research dataset there are 1459 instances classified as “Alive” while 485 are classified as “Dead”. Machine learning is gene...

The performance of the data classification has encountered a problem when distribution is imbalanced. This fact results in classifiers tend to majority class which most instances. One popular approaches balance dataset using over and under sampling methods. paper presents novel pre-processing technique that performs both algorithms for an imbalanced dataset. proposed method uses SMOTE algorithm...

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...

In recent years, mining with imbalanced data sets receives more and more attentions in both theoretical and practical aspects. This paper introduces the importance of imbalanced data sets and their broad application domains in data mining, and then summarizes the evaluation metrics and the existing methods to evaluate and solve the imbalance problem. Synthetic minority oversampling technique (S...

One of the more challenging problems faced by the data mining community is that of imbalanced datasets. In imbalanced datasets one class (sometimes severely) outnumbers the other class, causing correct, and useful predictions to be difficult to achieve. In order to combat this, many techniques have been proposed, especially centered around sampling methods. In this paper we propose an ensemble ...

A dataset is said to be imbalanced when its classes are disproportionately represented in terms of the number of instances they contain. This problem is common in applications such as medical diagnosis of rare diseases, detection of fraudulent calls, signature recognition. In this paper we propose an alternative method for imbalanced learning, which balances the dataset using an undersampling s...

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...