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Effect of the Sampling of a Dataset in the Hyperparameter Optimization Phase over the Efficiency of a Machine Learning Algorithm
Joint Authors
DeCastro-García, Noemí
Muñoz Castañeda, Ángel Luis
Escudero García, David
Carriegos, Miguel V.
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-02-04
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract 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.
American Psychological Association (APA)
DeCastro-García, Noemí& Muñoz Castañeda, Ángel Luis& Escudero García, David& Carriegos, Miguel V.. 2019. Effect of the Sampling of a Dataset in the Hyperparameter Optimization Phase over the Efficiency of a Machine Learning Algorithm. Complexity،Vol. 2019, no. 2019, pp.1-16.
https://search.emarefa.net/detail/BIM-1132391
Modern Language Association (MLA)
DeCastro-García, Noemí…[et al.]. Effect of the Sampling of a Dataset in the Hyperparameter Optimization Phase over the Efficiency of a Machine Learning Algorithm. Complexity No. 2019 (2019), pp.1-16.
https://search.emarefa.net/detail/BIM-1132391
American Medical Association (AMA)
DeCastro-García, Noemí& Muñoz Castañeda, Ángel Luis& Escudero García, David& Carriegos, Miguel V.. Effect of the Sampling of a Dataset in the Hyperparameter Optimization Phase over the Efficiency of a Machine Learning Algorithm. Complexity. 2019. Vol. 2019, no. 2019, pp.1-16.
https://search.emarefa.net/detail/BIM-1132391
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1132391