Fuzzy support vector machine (FSVM) has been combined with class imbalance learning
(CIL) strategies to address the problem of classifying skewed data. However, the existing …

The learning problem from imbalanced data sets poses a major challenge in data mining
community. Although conventional support vector machine can generally show relatively …

Imbalanced problem occurs when the size of the positive class is much smaller than that of
the negative one. Positive class usually refers to the main interest of the classification task …

This work is an extension of the Fuzzy Support Vector Machines for Class Imbalance
Learning (FSVM-CIL) method proposed by Rukshan Batuwita and Vasile Palade. For …

Support vector machines (SVMs) is a popular machine learning technique, which works
effectively with balanced datasets. However, when it comes to imbalanced datasets, SVMs …

Datasets that have skewed class distributions pose a difficulty to learning algorithms in
pattern classification. A number of different methods to deal with this problem have been …

Recently a robust and efficient classifier termed affinity and class probability-based fuzzy
support vector machine (ACFSVM) was proposed to address the binary class imbalance and …

The class imbalance problems have been reported to severely hinder classification
performance of many standard learning algorithms, and have attracted a great deal of …

Abstract Support Vector Machine (SVM) has been widely developed for tackling
classification problems. Imbalanced data exist in many practical classification problems …

As a kernel-based method, whether the selected kernel matches the data determines the
performance of support vector machine. Conventional support vector classifiers are not …