Bandit-Based Automated Machine Learning
| Autor: | Duncan D. Ruiz, Carlos Soares, Silvia N. das Dôres |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry Feature extraction Bayesian optimization 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Field (computer science) Range (mathematics) Workflow Hyperparameter optimization 0202 electrical engineering electronic engineering information engineering Task analysis Preprocessor 020201 artificial intelligence & image processing Artificial intelligence business computer 0105 earth and related environmental sciences |
| Zdroj: | BRACIS |
| DOI: | 10.1109/bracis.2018.00029 |
| Popis: | Machine Learning (ML) has been successfully applied to a wide range of domains and applications. Since the number of ML applications is growing, there is a need for tools that boost the data scientist's productivity. Automated Machine Learning (AutoML) is the field of ML that aims to address these needs through the development of solutions which enable data science practitioners, experts and non-experts, to efficiently create fine-tuned predictive models with minimum intervention. In this paper, we present the application of the multi-armed bandit optimization algorithm Hyperband to address the AutoML problem of generating customized classification workflows, a combination of preprocessing methods and ML algorithms including hyperparameter optimization. Experimental results comparing the bandit-based approach against Auto ML Bayesian Optimization methods show that this new approach is superior to the state-of-the-art methods in the test evaluation and equivalent to them in a statistical analysis. |
| Databáze: | OpenAIRE |
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