Accelerating the computation of shapley effects for datasets with many observations
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| Tag | 1 | 2 | Value |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20260002972 | ||
| 003 | MAP | ||
| 005 | 20260211184612.0 | ||
| 008 | 260206e20250811che|||p |0|||b|eng d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a6 | ||
| 100 | $0MAPA20260002330$aRabitti, Giovanni | ||
| 245 | 1 | 0 | $aAccelerating the computation of shapley effects for datasets with many observations$cGiovanni Rabitti and George Tzougas |
| 520 | $aThe document presents a strategy to accelerate the computation of Shapley effects, a sensitivity-analysis method used to identify the importance of risk factors in actuarial models. The traditional procedure becomes computationally expensive when dealing with large datasets. The authors propose reducing the sample size using techniques such as Latin Hypercube Sampling, Conditional Latin Hypercube Sampling, and Hierarchical k-means, selecting representative observations while preserving calculation accuracy. They apply this methodology to the well-known French automobile claim-frequency dataset, demonstrating drastic reductions in computation time with minimal loss of precision. The study concludes that this approach enables efficient estimation of Shapley effects even in big-data contexts, providing a relevant advancement for actuarial modeling and insurance risk analysis | ||
| 650 | 4 | $0MAPA20140022717$aBig data | |
| 650 | 4 | $0MAPA20080592011$aModelos actuariales | |
| 650 | 4 | $0MAPA20140007837$aClusters | |
| 650 | 4 | $0MAPA20080570651$aSiniestralidad | |
| 650 | 4 | $0MAPA20170005476$aMachine learning | |
| 700 | 1 | $0MAPA20140009800$aTzougas, George | |
| 710 | 2 | $0MAPA20200009078$aSpringer Nature | |
| 773 | 0 | $wMAP20220007085$g11/08/2025 Volume 15 - Number 2 - August 2025 , p. 885 - 898$tEuropean Actuarial Journal$dCham, Switzerland : Springer Nature Switzerland AG, 2021-2022 |