A one-class classification approach based on SVDD for imbalanced and overlapping classes Online publication date: Fri, 07-Jan-2022
by SeyyedMohammad JavadiMoghaddam; Reyhane Rateghi
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 13, No. 4, 2021
Abstract: Imbalanced data classification is a challenge especially when there is overlapping between two classes. The overlap makes it almost impossible to create differences in the two classes and isolate them. In the real world, many of the datasets are imbalanced and overlapped. This paper identifies the overlapping regions optimally by comparing the results of a single-class support vector data description (SVDD) algorithm performed on each class. Then, the method uses the nearest-neighbour algorithm to classify the data in the overlapping region. The result of the evaluation on the datasets with a high imbalanced rate shows better performance than other approaches.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Analysis Techniques and Strategies (IJDATS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com