Introduction: Breast cancer is the second cause of mortality among women. Early detection is the only rescue to reduce the risk of breast cancer mortality. Traditional methods cannot effectively diagnose tumor since they are based on the assumption of well-balanced dataset.. However, a hybrid method can help to alleviate the two-class imbalance problem existing in the ...

In classification tasks, imbalance data causes the inadequate predictive performance of a tiny minority class because the decision boundary determined by trivial classifiers tends to be biased toward a huge majority class. For handling the class imbalance problem, overand undersampling are applied at the data level. Over-sampling duplicates or synthesizes instances into a minority class. Althou...

Most medical datasets are not balanced in their class labels. Furthermore, in some cases it has been noticed that the given class labels do not accurately represent characteristics of the data record. Most existing classification methods tend not to perform well on minority class examples when the dataset is extremely imbalanced. This is because they aim to optimize the overall accuracy without...

Although class imbalance learning and online learning have been extensively studied in the literature separately, online class imbalance learning that considers the challenges of both ̄elds has not drawn much attention. It deals with data streams having very skewed class distributions, such as fault diagnosis of real-time control monitoring systems and intrusion detection in computer networks. T...

In data mining, large differences in prior class probabilities known as the class imbalance problem have been reported to hinder the performance of classifiers such as decision trees. Dealing with imbalanced and cost-sensitive data has been recognized as one of the 10 most challenging problems in data mining research. In decision trees learning, many measures are based on the concept of Shannon...

Nowadays learning from imbalanced data sets are a relatively a very critical task for many data mining applications such as fraud detection, anomaly detection, medical diagnosis, information retrieval systems. The imbalanced learning problem is nothing but unequal distribution of data between the classes where one class contains more and more samples while another contains very little. Because ...

We introduce an approach to learning from imbalanced class distributions that does not change the underlying data distribution. The ICC algorthm decomposes majority classes into smaller subclasses that create a more balanced class distribution. In this paper, we explain how ICC can not only address the class imbalance problem but may also increase the expressive power of the hypothesis space. W...

Protein-nucleotide interactions are ubiquitous in a wide variety of biological processes. Accurately identifying interaction residues solely from protein sequences is useful for both protein function annotation and drug design, especially in the post-genomic era, as large volumes of protein data have not been functionally annotated. Protein-nucleotide binding residue prediction is a typical imb...

Evaluating classifier performance with ROC curves is popular in the machine learning community. To date, the only method to assess confidence of ROC curves is to construct ROC bands. In the case of severe class imbalance, ROC bands become unreliable. We propose a generic framework for classifier evaluation to identify the confident segment of an ROC curve. Confidence is measured by Tango’s 95%-...

Customer response is a crucial aspect of service business. The ability to accurately predict which customer profiles are productive has proven invaluable in customer relationship management. An area that has received little attention in the literature on direct marketing is the class imbalance problem (the very low response rate). We propose a customer response predictive model approach combini...