| Autor: |
Jorge Luis Andrade, José Luis Valencia |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Mathematics, Vol 11, Iss 1, p 198 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2227-7390 |
| DOI: |
10.3390/math11010198 |
| Popis: |
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-event data using an ensemble of multiple fuzzy survival trees. In the learning process, the combination of methods such as the c-index, fuzzy sets theory, and the ensemble of multiple trees enable the automatic handling of imprecise data. We analyse the results of several experiments and test them statistically; they show the FRSF’s robustness, verifying that its generalisation capacity is not reduced when modelling imprecise data. Furthermore, the results obtained using a real portfolio of a life insurance company demonstrate that the FRSF has a better performance in comparison with other state-of-the-art algorithms such as the traditional Cox model and other tree-based machine learning techniques such as the random survival forest. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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