RT info:eu-repo/semantics/article
T1 RHOASo: An Early Stop Hyper-Parameter Optimization Algorithm
A1 Muñoz Castañeda, Ángel Luis
A1 Castro García, Noemí de
A1 Escudero García, David
A2 Algebra
K1 Ingeniería de sistemas
K1 Matemáticas
K1 Hyperparameters
K1 Machine learning
K1 Optimization; inference
K1 1203.02 Lenguajes Algorítmicos
K1 1207.02 Sistemas de Control
K1 1203.04 Inteligencia Artificial
AB [EN] This work proposes a new algorithm for optimizing hyper-parameters of a machine learning algorithm, RHOASo, based on conditional optimization of concave asymptotic functions. A comparative analysis of the algorithm is presented, giving particular emphasis to two important properties: the capability of the algorithm to work efficiently with a small part of a dataset and to finish the tuning process automatically, that is, without making explicit, by the user, the number of iterations that the algorithm must perform. Statistical analyses over 16 public benchmark datasets comparing the performance of seven hyper-parameter optimization algorithms with RHOASo were carried out. The efficiency of RHOASo presents the positive statistically significant differences concerning the other hyper-parameter optimization algorithms considered in the experiments. Furthermore, it is shown that, on average, the algorithm needs around 70%  of the iterations needed by other algorithms to achieve competitive performance. The results show that the algorithm presents significant stability regarding the size of the used dataset partition.
PB MDPI
LK https://hdl.handle.net/10612/19441
UL https://hdl.handle.net/10612/19441
NO Castañeda, Á. L. M., Decastro-García, N., & Escudero García, D. (2021). Rhoaso: An early stop hyper-parameter optimization algorithm. Mathematics, 9(18). https://doi.org/10.3390/MATH9182334
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 21-may-2024