A Robust Experimental Evaluation of Automated Multi-Label Classification Methods

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{deSa:2020:GECCO,

• author = "Alex G. C. {de Sa} and Cristiano G. Pimenta and Gisele L. Pappa and Alex A. Freitas",
• title = "A Robust Experimental Evaluation of Automated Multi-Label Classification Methods",
• year = "2020",
• editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann",
• isbn13 = "9781450371285",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• URL = "https://doi.org/10.1145/3377930.3390231",
• DOI = "doi:10.1145/3377930.3390231",
• booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference",
• pages = "175--183",
• size = "9 pages",
• keywords = "genetic algorithms, genetic programming, search spaces, automated machine learning (AutoML), search methods, multi-label classification",
• address = "internet",
• series = "GECCO '20",
• month = jul # " 8-12",
• organisation = "SIGEVO",
• abstract = "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 effective methods were introduced, especially for traditional classification and regression problems. Apart from the AutoML success, several issues remain open. One issue, in particular, is the lack of ability of AutoML methods to deal with different types of data. Based on this scenario, this paper approaches AutoML for multi-label classification (MLC) problems. In MLC, each example can be simultaneously associated to several class labels, unlike the standard classification task, where an example is associated to just one class label. 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. Overall, we observe that the most prominent method is the one based on a canonical grammar-based genetic programming (GGP) search method, namely Auto-MEKAGGP. Auto-MEKAGGP presented the best average results in our comparison and was statistically better than all the other methods in different search spaces and evaluated measures, except when compared to the greedy search method.",
• notes = "Also known as \cite{10.1145/3377930.3390231} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Alex G C de Sa Cristiano Guimaraes Pimenta Gisele L Pappa Alex Alves Freitas

Citations