Depth-based support vector classifiers to detect data nests of rare events
by Rainer Dyckerhoff; Hartmut Jakob Stenz
International Journal of Computational Economics and Econometrics (IJCEE), Vol. 11, No. 2, 2021

Abstract: The aim of this project is to combine data depth with support vector machines (SVM) for binary classification. To this end, we introduce data depth functions and SVM and discuss why a combination of the two is assumed to work better in some cases than using SVM alone. For two classes X and Y , one investigates whether an individual data point should be assigned to one of these classes. In this context, our focus lies on the detection of rare events, which are structured in data nests: class X contains much more data points than class Y and Y has less dispersion than X. This form of classification problem is akin to finding the proverbial needle in a haystack. Data structures like these are important in churn prediction analyses which will serve as a motivation for possible applications. Beyond the analytical investigations, comprehensive simulation studies will also be carried out.

Online publication date: Tue, 27-Apr-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
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 Computational Economics and Econometrics (IJCEE):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your 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