Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms-Reference-Cited by-同舟云学术

Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms

Published:2024-01-31 Issue: Volume: Page:
ISSN:1384-5810
Container-title:Data Mining and Knowledge Discovery
language:en
Short-container-title:Data Min Knowl Disc

Author:

Gomes Mantovani Rafael,Horváth Tomáš,Rossi André L. D.,Cerri Ricardo,Barbon Junior Sylvio,Vanschoren Joaquin,Carvalho André C. P. L. F. de

Funder

Fundação de Amparo à Pesquisa do Estado de São Paulo

Talent Management in Autonomous Vehicle Control Technologies -- The Project is supported by the Hungarian Government and cofinanced by the European Social Fund

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Computer Science Applications,Information Systems

Link

https://link.springer.com/content/pdf/10.1007/s10618-024-01002-5.pdf

Reference106 articles.

1. Abe S (2005) Support vector machines for pattern classification. Springer, London

2. Alcobaça E, Siqueira F, Rivolli A et al (2020) MFE: towards reproducible meta-feature extraction. J Mach Learn Res 21:111:1-111:5

3. Ali S, Smith-Miles KA (2006) A meta-learning approach to automatic kernel selection for support vector machines. Neurocomputing 70(13):173–186

4. Andradottir S (2015) A review of random search methods. In: Fu MC (ed) Handbook of simulation optimization, international series in operations research & management science, vol 216. Springer, New York, pp 277–292

5. Bache K, Lichman M (2013) UCI machine learning repository. http://archive.ics.uci.edu/ml