Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Depth-based support vector classifiers to detect data nests of rare events

Depth-based support vector classifiers to detect data nests of rare events 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Computational Economics and Econometrics Inderscience Publishers

Depth-based support vector classifiers to detect data nests of rare events

Download PDF
36 pages

Loading next page...
 
/lp/inderscience-publishers/depth-based-support-vector-classifiers-to-detect-data-nests-of-rare-7g0bq2fY8Z

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1757-1170
eISSN
1757-1189
DOI
10.1504/IJCEE.2021.114550
Publisher site
See Article on Publisher Site

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.

Journal

International Journal of Computational Economics and Econometrics – Inderscience Publishers

Published: Jan 1, 2021

There are no references for this article.