In recent decades, researchers have proposed different boosting algorithms to improve the
learning abilities of classification techniques for imbalanced data. Especially for the SVM …

Support vector machine (SVM) is a powerful tool for pattern classification and regression
estimation. However, for the class imbalanced problem, conventional SVMs are not suitable …

Imbalanced data classification poses a major challenge in data mining community. Although
standard support vector machine can generally show relatively robust performance in …

In real datasets, most are unbalanced. Data imbalance can be defined as the number of
instances in some classes greatly exceeds the number of instances in other classes …

Traditional classification algorithms can be limited in their performance on highly
imbalanced and overlapping data sets, In this paper, we focus on modifying support vector …

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

In imbalanced learning methods, resampling methods modify an imbalanced dataset to form
a balanced dataset. Balanced data sets perform better than imbalanced datasets for many …

Class imbalance classification is a demanding research problem in the context of machine
learning and its applications, as most of the real-life datasets are often imbalanced in nature …

Support vector machine (SVM) is one of the most popular algorithms in machine learning
and data mining. However, its reduced efficiency is usually observed for imbalanced …

Class imbalance problem has been a challenging research problem in the fields of machine
learning and data mining as most real life datasets are imbalanced. Several existing …