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| dc.contributor | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
|---|---|
| dc.contributor | Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge |
| dc.contributor.author | Arias Vicente, Marta |
| dc.contributor.author | Arratia Quesada, Argimiro Alejandro |
| dc.contributor.author | Duarte López, Ariel |
| dc.date | 2017 |
| dc.identifier.citation | Arias, M., Arratia, A., Duarte, A. Classifier selection with permutation tests. A: International Conference of the Catalan Association for Artificial Intelligence. "Recent Advances in Artificial Intelligence Research and Development: Proceedings of the 20th International Conference of the Catalan Association for Artificial Intelligence, Deltebre, Terres de l'Ebre, Spain, October 25–27, 2017". IOS Press, 2017, p. 96-105. |
| dc.identifier.citation | 978-1-61499-805-1 |
| dc.identifier.citation | https://arxiv.org/abs/1711.09708 |
| dc.identifier.citation | 10.3233/978-1-61499-806-8-96 |
| dc.identifier.uri | http://hdl.handle.net/2117/114678 |
| dc.description.abstract | This work presents a content-based recommender system for machine learning classifier algorithms. Given a new data set, a recommendation of what classifier is likely to perform best is made based on classifier performance over similar known data sets. This similarity is measured according to a data set characterization that includes several state-of-the-art metrics taking into account physical structure, statistics, and information theory. A novelty with respect to prior work is the use of a robust approach based on permutation tests to directly assess whether a given learning algorithm is able to exploit the attributes in a data set to predict class labels, and compare it to the more commonly used F-score metric for evaluating classifier performance. To evaluate our approach, we have conducted an extensive experimentation including 8 of the main machine learning classification methods with varying configurations and 65 binary data sets, leading to over 2331 experiments. Our results show that using the information from the permutation test clearly improves the quality of the recommendations. |
| dc.description.abstract | Peer Reviewed |
| dc.language.iso | eng |
| dc.publisher | IOS Press |
| dc.relation | http://ebooks.iospress.nl/volumearticle/47729 |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
| dc.rights | info:eu-repo/semantics/openAccess |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
| dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| dc.subject | Machine learning |
| dc.subject | Computer algorithms |
| dc.subject | Data mining |
| dc.subject | Information theory |
| dc.subject | Learning algorithms |
| dc.subject | Learning systems |
| dc.subject | Statistical tests |
| dc.subject | Aprenentatge automàtic |
| dc.subject | Algorismes computacionals |
| dc.title | Classifier selection with permutation tests |
| dc.type | info:eu-repo/semantics/submittedVersion |
| dc.type | info:eu-repo/semantics/conferenceObject |
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