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On the predictive power of meta-features in OpenML

Autor
Bilalli, B.; Abello, A.; Aluja, T.
Tipus d'activitat
Article en revista
Revista
International journal of applied mathematics and computer science
Data de publicació
2017-12-20
Volum
27
Número
4
Pàgina inicial
697
Pàgina final
712
DOI
https://doi.org/10.1515/amcs-2017-0048 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/113229 Obrir en finestra nova
URL
https://www.degruyter.com/view/j/amcs.2017.27.issue-4/amcs-2017-0048/amcs-2017-0048.xml Obrir en finestra nova
Resum
The demand for performing data analysis is steadily rising. As a consequence, people of different profiles (i.e., non-experienced users) have started to analyze their data. However, this is challenging for them. A key step that poses difficulties and determines the success of the analysis is data mining (model/algorithm selection problem). Meta-learning is a technique used for assisting non-expert users in this step. The effectiveness of meta-learning is, however, largely dependent on the descri...
Citació
Bilalli, B., Abello, A., Aluja, T. On the predictive power of meta-features in OpenML. "International journal of applied mathematics and computer science", 20 Desembre 2017, vol. 27, núm. 4, p. 697-712.
Paraules clau
feature extraction, feature selection, meta-learning
Grup de recerca
GESSI - Grup d'Enginyeria del Software i dels Serveis
LIAM - Laboratori de Modelització i Anàlisi de la Informació

Participants

Arxius