A Fuzzy Random Survival Forest for Predicting Lapses in Insurance Portfolios Containing Imprecise Data
We propose a fuzzy random survival forest (FRSF) to model lapse rates in a life insurance portfolio containing imprecise or incomplete data such as missing, outlier, or noisy values. Following the random forest methodology, the FRSF is proposed as a new machine learning technique for solving time-to...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/11/1/198 |