Book section : Chapter
Topological analysis of credit data: preliminary findings
- Abstract:
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There is plenty of room for improvement in credit risk prediction. Intuitively, similar customers should have similar credit risk. Capturing this similarity is often carried out using Euclidean distances between customer features and predicting credit default via logistic regression. Here we explore the use of topological data analysis for describing this similarity. In particular, persistent homology algorithms provide summaries of point clouds which relate to their topology. This approach h...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
-
-
(Accepted manuscript, pdf, 749.7KB)
-
- Publisher copy:
- 10.1007/978-3-031-21753-1_42
Authors
Bibliographic Details
- Publisher:
- Springer Publisher's website
- Host title:
- Proceedings of the 23rd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)
- Series:
- Lecture Notes in Computer Science
- Volume:
- 13756
- Pages:
- 432–442
- Publication date:
- 2022-11-21
- DOI:
- EISSN:
-
1611-3349
- ISSN:
-
0302-9743
- EISBN:
- 9783031217531
- ISBN:
- 9783031217524
Item Description
- Language:
- English
- Keywords:
- Subtype:
- Chapter
- Pubs id:
-
1308853
- Local pid:
- pubs:1308853
- Deposit date:
- 2022-11-25
Terms of use
- Copyright holder:
- Cooper et al
- Copyright date:
- 2022
- Rights statement:
- © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
- Notes:
- This paper was presented at the 23rd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2022), 24th-26th November 2022, Manchester, UK. This is the accepted manuscript version of the article. The final version is available online from Springer at: https://doi.org/10.1007/978-3-031-21753-1_42
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