fuzzy rule-based classification system (frbcs) is a popular machine learning technique for classification purposes. one of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. however many cases the minority classes are more important than the majority ones. in this paper, we have extended ...

credit scoring is a classification problem leading to introducing numerous techniques to deal with it such as support vector machines, neural networks and rule-based classifiers. rule bases are the top priority in credit decision making because of their ability to explicitly distinguish between good and bad applicants.in a credit- scoring context, imbalanced data sets frequently occur as the nu...

Fuzzy rule-based classification system (FRBCS) is a popular machine learning technique for classification purposes. One of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. However many cases the minority classes are more important than the majority ones. In this paper, we have extended ...

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...

Learning from data with severe class imbalance is difficult. Established solutions include: under-sampling, adjusting classification threshold, and using an ensemble. We examine the performance of combining these solutions to balance the sensitivity and specificity for binary classifications, and to reduce the MSE score for probability estimation.

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 ...