38 A review of: Effect of the Sampling of a Dataset in the Hyperparameter Optimization Phase over the Efficiency of a Machine Learning Algorithm
Cybersecurity is a discipline in which artificial intelligence techniques are gaining in importance in order to obtain actionable knowledge. The selection of a fitting configuration of hyperparameters is an important factor in model performance. Several hyperparameter optimization algoritms have bee...
| Autores: | , , |
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| Tipo de recurso: | capítulo de libro |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad de Castilla-La Mancha |
| Repositorio: | RUIdeRA. Repositorio Institucional de la UCLM |
| OAI Identifier: | oai:ruidera.uclm.es:10578/28648 |
| Acceso en línea: | http://doi.org/10.18239/jornadas_2021.34.38 http://hdl.handle.net/10578/28648 |
| Access Level: | acceso abierto |
| Palabra clave: | cybersegurity Machine Learning Hyperparameter optimization |
| Sumario: | Cybersecurity is a discipline in which artificial intelligence techniques are gaining in importance in order to obtain actionable knowledge. The selection of a fitting configuration of hyperparameters is an important factor in model performance. Several hyperparameter optimization algoritms have been developed, but their application imposes additional computational costs. Since one of the main factors in the resource consumption is the dataset size, we perform a study of the effect of using different partitions over five different cybersecurity datasets. Nonparametric inference has been used to measure the rate of change of the accuracy, time, and spatial (memory) complexity along the partition size. In addition, a level of gain is assigned to each partition allowing us to study patterns and determine the optimal partition size. |
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