Learning from imbalanced datasets has drawn more and more attentions from both theoretical and practical aspects. Over-sampling is a popular and simple method for imbalanced learning. In this paper, we show that there is an inherently potential risk associated with the oversampling algorithms in terms of the large margin principle. Then we propose a new synthetic over sampling method, named Mar...

Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature. Existing learning algorithms maximise the classification accuracy by correctly classifying the majority class, but misclassify the minority class. However, the minority class instances are representing the concept with greater in...

Class imbalance is one of the challenging problems for machine learning in many real-world applications. Other issues, such as within-class imbalance and high dimensionality, can exacerbate the problem. We propose a method HPSDRS that combines two ideas: Hybrid Probabilistic Sampling technique ensemble with Diverse Random Subspace to address these issues. HPS improves the performance of traditi...

Fraud detection has received considerable attention from many academic research and industries worldwide due to its increasing popularity. Insurance datasets are enormous, with skewed distributions high dimensionality. Skewed class distribution volume considered significant problems while analyzing insurance datasets, as these issues increase the misclassification rates. Although sampling appro...

Imbalanced class problems appear in many real applications of classification learning. We propose a novel sampling method to improve bagging for data sets with skewed class distributions. In our new sampling method “Roughly Balanced Bagging” (RB Bagging), the number of samples in the largest and smallest classes are different, but they are effectively balanced when averaged over all subsets, wh...

Learning from imbalanced datasets is inherently difficult due to lack of information about the minority class. In this paper, we study the performance of SVMs, which have gained great success in many real applications, in the imbalanced data context. Through empirical analysis, we show that SVMs suffer from biased decision boundaries, and that their prediction performance drops dramatically whe...

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...