- Tytuł:
- Stochastic schemata exploiter-based optimization of hyper-parameters for XGBoost
- Autorzy:
-
Makino, Hiroya
Kita, Eisuke - Powiązania:
- https://bibliotekanauki.pl/articles/38707755.pdf
- Data publikacji:
- 2024
- Wydawca:
- Instytut Podstawowych Problemów Techniki PAN
- Tematy:
-
evolutionary computation
Stochastic Schemata Exploiter
hyperparameter optimization
XGBoost
obliczenia ewolucyjne
eksplorator schematów stochastycznych
optymalizacja hiperparametrów - Opis:
- XGBoost is well-known as an open-source software library that provides a regularizing gradient boosting framework. Although it is widely used in the machine learning field, its performance depends on the determination of hyper-parameters. This study focuses on the optimization algorithm for hyper-parameters of XGBoost by using Stochastic Schemata Exploiter (SSE). SSE, which is one of Evolutionary Algorithms, is successfully applied to combinatorial optimization problems. SSE is applied for optimizing hyper-parameters of XGBoost in this study. The original SSE algorithm is modified for hyper-parameter optimization. When comparing SSE with a simple Genetic Algorithm, there are two interesting features: quick convergence and a small number of control parameters. The proposed algorithm is compared with other hyper-parameter optimization algorithms such as Gradient Boosted Regression Trees (GBRT), Tree-structured Parzen Estimator (TPE), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and Random Search in order to confirm its validity. The numerical results show that SSE has a good convergence property, even with fewer control parameters than other methods.
- Źródło:
-
Computer Assisted Methods in Engineering and Science; 2024, 31, 1; 113-132
2299-3649 - Pojawia się w:
- Computer Assisted Methods in Engineering and Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł