Depth-based support vector classifiers to detect data nests of rare events
Rainer Dyckerhoff, Hartmut Jakob Stenz
| Year of publication: |
2021
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| Authors: | Dyckerhoff, Rainer ; Stenz, Hartmut Jakob |
| Published in: |
International journal of computational economics and econometrics : IJCEE. - Genève [u.a.] : Inderscience Enterprises, ISSN 1757-1189, ZDB-ID 2545120-0. - Vol. 11.2021, 2, p. 107-142
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| Subject: | data depth | DD-plot | Mahalanobis depth function | support vector machines | SVM | binary classification | hybrid methods | rare events | data nest | churn prediction | big data | Mustererkennung | Pattern recognition | Prognoseverfahren | Forecasting model | Data Mining | Data mining | Big Data | Big data | Klassifikation | Classification | Theorie | Theory | Statistische Methode | Statistical method |
Online Resource
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1504/IJCEE.2021.114550 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10012507582