Abstract
Automatically searching for optimal hyper parameters is of crucial importance for applying machine learning algorithms in practice. However, there are concerns regarding the tradeoff between efficiency and effectiveness of current approaches when faced with the expensive function evaluations. In this paper, a novel efficient hyper-parameter optimization algorithm is proposed (called MARSAOP), in which multivariate spline functions are used as surrogate and dynamic coordinate search approach is employed to generate the candidate points. Empirical studies on benchmark problems and machine-learning models (e.g., SVM, RF, and NN) demonstrate that the proposed algorithm is able to find relatively high-quality solutions for benchmark problems and excellent hyper-parameter configurations for machine-learning models using a limited computational budget (few function evaluations).
Original language | English |
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Article number | 8735959 |
Pages (from-to) | 287-297 |
Number of pages | 11 |
Journal | IEEE Transactions on Emerging Topics in Computational Intelligence |
Volume | 4 |
Issue number | 3 |
DOIs | |
Publication status | Published - Jun 2020 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
Keywords
- dynamic coordinate search
- Hyper-parameter optimization
- machine learning
- MARS
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Computational Mathematics
- Control and Optimization