Bandit-Based Automated Machine Learning

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Silvia Nunes das Dores (Author) Other; Carlos Soares (Author) FEUP; Duncan Ruiz (Author) Other

Conference proceedings International

Title: 2018 7TH BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS)

Pages: 121-126

7th Brazilian Conference on Intelligent Systems (BRACIS)

IBM Res, Sao Paulo, BRAZIL, OCT 22-25, 2018

Indexing

ISI Web of Knowledge - 7 Citations

Scopus - 8 Citations

INSPEC

Other information

Authenticus ID: P-00Q-2M7

DOI: 10.1109/bracis.2018.00029

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6