no code implementations • 19 Nov 2020 • Jerry Swan, Steven Adriaensen, Alexander E. I. Brownlee, Kevin Hammond, Colin G. Johnson, Ahmed Kheiri, Faustyna Krawiec, J. J. Merelo, Leandro L. Minku, Ender Özcan, Gisele L. Pappa, Pablo García-Sánchez, Kenneth Sörensen, Stefan Voß, Markus Wagner, David R. White
We describe our vision and report on progress, showing how the adoption of common protocols for all metaheuristics can help liberate the potential of the field, easing the exploration of the design space of metaheuristics.
no code implementations • 16 Sep 2020 • Márcio P. Basgalupp, Rodrigo C. Barros, Alex G. C. de Sá, Gisele L. Pappa, Rafael G. Mantovani, André C. P. L. F. de Carvalho, Alex A. Freitas
Auto-WEKA combines algorithm selection and hyper-parameter optimisation to recommend classification algorithms from multiple paradigms.
1 code implementation • 31 Jul 2020 • Matheus Nunes, Gisele L. Pappa
However, they possess a large number of hyperparameters and their design and optimization is currently hand-made, based on heuristics or empirical intuition.
1 code implementation • 16 May 2020 • Alex G. C. de Sá, Cristiano G. Pimenta, Gisele L. Pappa, Alex A. Freitas
In this work, we provide a general comparison of five automated multi-label classification methods -- two evolutionary methods, one Bayesian optimization method, one random search and one greedy search -- on 14 datasets and three designed search spaces.
1 code implementation • 28 Nov 2018 • Alex G. C. de Sá, Cristiano G. Pimenta, Gisele L. Pappa, Alex A. Freitas
This supplementary material aims to describe the proposed multi-label classification (MLC) search spaces based on the MEKA and WEKA softwares.
1 code implementation • 18 Apr 2018 • Joao Francisco B. S. Martins, Luiz Otavio V. B. Oliveira, Luis F. Miranda, Felipe Casadei, Gisele L. Pappa
Advances in Geometric Semantic Genetic Programming (GSGP) have shown that this variant of Genetic Programming (GP) reaches better results than its predecessor for supervised machine learning problems, particularly in the task of symbolic regression.
no code implementations • 4 Jul 2017 • Luis F. Miranda, Luiz Otavio V. B. Oliveira, Joao Francisco B. S. Martins, Gisele L. Pappa
The results show that, as we increase the percentage of noisy instances, the generalization performance degradation is more pronounced in GSGP than GP.
no code implementations • 15 Mar 2017 • Fabricio Murai, Diogo Rennó, Bruno Ribeiro, Gisele L. Pappa, Don Towsley, Krista Gile
We find that it is possible to collect a much larger set of targets by using multiple classifiers, not by combining their predictions as an ensemble, but switching between classifiers used at each step, as a way to ease the tunnel vision effect.
no code implementations • 25 Feb 2016 • Tiago O. Cunha, Ingmar Weber, Hamed Haddadi, Gisele L. Pappa
It is generally accepted as common wisdom that receiving social feedback is helpful to (i) keep an individual engaged with a community and to (ii) facilitate an individual's positive behavior change.
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