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Abstract
Our goal is to do risk prediction in credit operations, and as data is collected continuously and reported on a monthly basis, this gives rise to a streaming data classification problem. Our analysis reveals some practical problems that have not previously been thoroughly analyzed in the context of streaming data analysis: the class labels are not immediately available and the relevant predictive features and entities under study (in this case the set of customers) may vary over time. In order to address these problems, we propose to use a dynamic classifier with a wrapper feature subset selection to find relevant features at different time steps. The proposed model is a special case of a more general framework that can also accommodate more expressive models containing latent variables as well as more sophisticated feature selection schemes.
Original language | English |
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Title of host publication | The 13th Scandinavian Conference on Artificial Intelligence (SCAI'2015) |
Publisher | IOS Press |
Publication date | 2015 |
Pages | 17-26 |
ISBN (Print) | 978-1-61499-588-3 |
ISBN (Electronic) | 978-1-61499-589-0 |
DOIs | |
Publication status | Published - 2015 |
Event | 13th Scandinavian Conference on Artificial Intelligence - Halmstad University, Halmstad, Sweden Duration: 4 Nov 2015 → 6 Nov 2015 Conference number: 13th |
Conference
Conference | 13th Scandinavian Conference on Artificial Intelligence |
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Number | 13th |
Location | Halmstad University |
Country/Territory | Sweden |
City | Halmstad |
Period | 04/11/2015 → 06/11/2015 |
Series | Frontiers in Artificial Intelligence and Applications |
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Volume | 278 |
ISSN | 0922-6389 |
Keywords
- Streaming data
- Dynamic Bayesian networks
- Variational Bayes
- Feature subset selection
- Credit operations
Fingerprint
Dive into the research topics of 'Dynamic Bayesian modeling for risk prediction in credit operations'. Together they form a unique fingerprint.Projects
- 1 Finished
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AMIDST: Analysis of MassIve Data STreams - AMIDST
Madsen, A. L., Rommerdahl Bock, A., Nielsen, T. D. & Martinez, A. M.
01/01/2014 → 31/12/2016
Project: Research