activity
20162020
most citedMetaheuristics "In the Large"

88 citations · 178 across the 12 of their papers we have counts for

collaborators

13 papers

cs.NE2020★ 88 cited

Metaheuristics "In the Large"

Jerry Swan, Steven Adriaensen, Alexander E. I. Brownlee +13

Following decades of sustained improvement, metaheuristics are one of the great success stories of optimization research. However, in order for research in metaheuristics to avoid…

cs.LG2020

An Extensive Experimental Evaluation of Automated Machine Learning Methods for Recommending Classification Algorithms (Extended Version)

Márcio P. Basgalupp, Rodrigo C. Barros, Alex G. C. de Sá +4

This paper presents an experimental comparison among four Automated Machine Learning (AutoML) methods for recommending the best classification algorithm for a given input dataset.…

cs.NE2020★ 5 cited

Neural Architecture Search in Graph Neural Networks

Matheus Nunes, Gisele L. Pappa

Performing analytical tasks over graph data has become increasingly interesting due to the ubiquity and large availability of relational information. However, unlike images or sent…

cs.LG2020★ 7 cited

A Robust Experimental Evaluation of Automated Multi-Label Classification Methods

Alex G. C. de Sá, Cristiano G. Pimenta, Gisele L. Pappa +1

Automated Machine Learning (AutoML) has emerged to deal with the selection and configuration of algorithms for a given learning task. With the progression of AutoML, several effect…

cs.LG2018★ 4 cited

Multi-label classification search space in the MEKA software

Alex G. C. de Sá, Cristiano G. Pimenta, Gisele L. Pappa +1

This supplementary material aims to describe the proposed multi-label classification (MLC) search spaces based on the MEKA and WEKA softwares. First, we overview 26 MLC algorithms…

cs.NE2018★ 10 cited

Analysing Symbolic Regression Benchmarks under a Meta-Learning Approach

Luiz Otavio Vilas Boas Oliveira, Joao Francisco Barreto da Silva Martins, Luis Fernando Miranda +1

The definition of a concise and effective testbed for Genetic Programming (GP) is a recurrent matter in the research community. This paper takes a new step in this direction, propo…