Computational Efficient Approximations of the Concordance Probability in a Big Data Setting-Reference-Cited by-同舟云学术

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Computational Efficient Approximations of the Concordance Probability in a Big Data Setting

Published:2023-06-07 Issue: Volume: Page:
ISSN:2167-6461
Container-title:Big Data
language:en
Short-container-title:Big Data

Author:

Oirbeek Robin Van¹,Ponnet Jolien²^ORCID,Baesens Bart³⁴^ORCID,Verdonck Tim²⁵^ORCID

Affiliation:

1. Data Office, Allianz Benelux, Brussels, Belgium.

2. Department of Mathematics, Faculty of Science, KU Leuven, Leuven, Belgium.

3. Faculty of Economics and Business, KU Leuven, Leuven, Belgium.

4. School of Management, University of Southampton, Southampton, United Kingdom.

5. Department of Mathematics, Faculty of Science, UAntwerp—imec, Antwerp, Belgium.

Publisher

Mary Ann Liebert Inc

Subject

Information Systems and Management,Computer Science Applications,Information Systems

Link

https://www.liebertpub.com/doi/pdf/10.1089/big.2022.0107

Reference17 articles.

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2. Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors

3. Deep Learning on Big, Sparse, Behavioral Data

4. The area above the ordinal dominance graph and the area below the receiver operating characteristic graph

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