Effect of the Sampling of a Dataset inthe Hyperparameter Optimization Phase over the Efficiency ofa Machine Learning Algorithm
[EN] Selecting the best configuration of hyperparameter values for a Machine Learning model yields directly in the performance of the model on the dataset. It is a laborious task that usually requires deep knowledge of the hyperparameter optimizations methods and the Machine Learning algorithms. Alt...
| Autores: | , , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2019 |
| País: | España |
| Recursos: | Universidad de León |
| Repositório: | BULERIA. Repositorio Institucional de la Universidad de León |
| OAI Identifier: | oai:buleria.unileon.es:10612/22050 |
| Acesso em linha: | https://onlinelibrary.wiley.com/doi/10.1155/2019/6278908 https://hdl.handle.net/10612/22050 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Cibernética Informática Computing methodologies Mathematics of computing Theory of computation Machine learning 1207.03 Cibernética 1203.04 Inteligencia Artificial 1203.24 Teoría de la Programación |
| Resumo: | [EN] Selecting the best configuration of hyperparameter values for a Machine Learning model yields directly in the performance of the model on the dataset. It is a laborious task that usually requires deep knowledge of the hyperparameter optimizations methods and the Machine Learning algorithms. Although there exist several automatic optimization techniques, these usually take significant resources, increasing the dynamic complexity in order to obtain a great accuracy. Since one of the most critical aspects in this computational consume is the available dataset, among others, in this paper we perform a study of the effect of using different partitions of a dataset in the hyperparameter optimization phase over the efficiency of a Machine Learning algorithm. Nonparametric inference has been used to measure the rate of different behaviors of the accuracy, time, and spatial complexity that are obtained among the partitions and the whole dataset. Also, a level of gain is assigned to each partition allowing us to study patterns and allocate whose samples are more profitable. Since Cybersecurity is a discipline in which the efficiency of Artificial Intelligence techniques is a key aspect in order to extract actionable knowledge, the statistical analyses have been carried out over five Cybersecurity datasets. |
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