Data in many biological problems are often compounded by imbalanced class distribution. That is, the positive examples may largely outnumbered by the negative examples. Many classification algorithms such as support vector machine (SVM) are sensitive to data with imbalanced class distribution, and result in a suboptimal classification. It is desirable to compensate the imbalance effect in model...

Class imbalance is a frequent problem found in bioinformatics datasets. Unfortunately, the minority class is usually also the class of interest. One of the methods to improve this situation is data sampling. There are a number of different data sampling methods, each with their own strengths and weaknesses, which makes choosing one a difficult prospect. In our work we compare three data samplin...

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.

For classification problem, the training data will significantly influence the classification accuracy. When the data set is highly unbalanced, classification algorithms tend to degenerate by assigning all cases to the most common outcome. Hence, it is important to select the suitable training data for classification in the imbalanced class distribution problem. In this paper, we propose cluste...

of the Thesis Classification of Imbalanced Data Using Synthetic Over-Sampling Techniques

0950-7051/$ see front matter 2011 Elsevier B.V. A doi:10.1016/j.knosys.2011.06.013 ⇑ Corresponding author. E-mail addresses: [email protected] (V. García), s [email protected] (R.A. Mollineda). The present paper investigates the influence of both the imbalance ratio and the classifier on the performance of several resampling strategies to deal with imbalanced data sets. The study focuses on evaluat...

Imbalanced learning, or learning from imbalanced data, is a challenging problem in both academy and industry. Nowadays, the streaming imbalanced data become popular and trigger the volume, velocity, and variety issues of learning from these data. To tackle these issues, online learning algorithms are proposed to learn a linear classifier via maximizing the AUC score. However, the developed line...