Imbalanced dataset classification issue poses a major challenge on machine learning
domain. Traditional supervised learning algorithms usually bias towards the majority class …

Learning from imbalanced datasets is inherently difficult due to lack of information about the
minority class. In this paper, we study the performance of SVMs, which have gained great …

An imbalanced data problem occurs in the absence of a good class distribution between
classes. Imbalanced data will cause the classifier to be biased to the majority class as the …

Ensemble technique and under-sampling technique are both effective tools used for
imbalanced dataset classification problems. In this paper, a novel ensemble method …

The problem of class imbalance along with class overlapping has become a major issue in
the domain of supervised learning. Most classification algorithms assume equal cardinality …

In the real-world applications of machine learning and cybernetics, the data with imbalanced
distribution of classes or skewed class proportions is very pervasive. When dealing with …

Abstract Support Vector Machine (SVM) learning from imbalanced datasets, as well as most
learning machines, can show poor performance on the minority class because SVMs were …

A novel method, namely ensemble support vector machine with segmentation (SeEn–SVM),
for the classification of imbalanced datasets is proposed in this paper. In particular, vector …

When the number of data in one class is significantly larger or less than the data in other
class, under machine learning algorithm for classification, a problem of learning …