dc.creator | Martínez Rojas, Antonio | es |
dc.creator | Jiménez Ramírez, Andrés | es |
dc.creator | González Enríquez, José | es |
dc.date.accessioned | 2022-04-28T10:17:47Z | |
dc.date.available | 2022-04-28T10:17:47Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Martínez Rojas, A., Jiménez Ramírez, A. y González Enríquez, J. (2019). Towards a unified model representation of machine learning knowledge. En WEBIST 2019 : 15th International Conference on Web Information Systems and Technologies (470-476), Vienna, Austria: SciTePress. | |
dc.identifier.isbn | 978-989-758-386-5 | es |
dc.identifier.issn | 2184-3252 | es |
dc.identifier.uri | https://hdl.handle.net/11441/132803 | |
dc.description.abstract | Nowadays, Machine Learning (ML) algorithms are being widely applied in virtually all possible scenarios.
However, developing a ML project entails the effort of many ML experts who have to select and configure
the appropriate algorithm to process the data to learn from, between other things. Since there exist thousands
of algorithms, it becomes a time-consuming and challenging task. To this end, recently, AutoML emerged to
provide mechanisms to automate parts of this process. However, most of the efforts focus on applying brute
force procedures to try different algorithms or configuration and select the one which gives better results.
To make a smarter and more efficient selection, a repository of knowledge is necessary. To this end, this
paper proposes (1) an approach towards a common language to consolidate the current distributed knowledge
sources related the algorithm selection in ML, and (2) a method to join the knowledge gathered through this
language in a unified store that can be exploited later on. The preliminary evaluations of this approach allow
to create a unified store collecting the knowledge of 13 different sources and to identify a bunch of research
lines to conduct. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2016-76956-C3-2-R | es |
dc.description.sponsorship | Centro para el Desarrollo Tecnológico Industrial P009-18/E09 | es |
dc.format | application/pdf | es |
dc.format.extent | 7 | es |
dc.language.iso | eng | es |
dc.publisher | SciTePress | es |
dc.relation.ispartof | WEBIST 2019 : 15th International Conference on Web Information Systems and Technologies (2019), pp. 470-476. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Machine Learning | es |
dc.subject | Automated Machine Learning | es |
dc.subject | Knowledge Representation | es |
dc.subject | Model-driven engineering | es |
dc.title | Towards a unified model representation of machine learning knowledge | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2016-76956-C3-2-R | es |
dc.relation.projectID | P009-18/E09 | es |
dc.relation.publisherversion | https://www.scitepress.org/Link.aspx?doi=10.5220/0008559204700476 | es |
dc.identifier.doi | 10.5220/0008559204700476 | es |
dc.contributor.group | Universidad de Sevilla. TIC021: Engineering and Science for Software Systems | es |
dc.publication.initialPage | 470 | es |
dc.publication.endPage | 476 | es |
dc.eventtitle | WEBIST 2019 : 15th International Conference on Web Information Systems and Technologies | es |
dc.eventinstitution | Vienna, Austria | es |
dc.relation.publicationplace | Setúbal, Portugal | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |
dc.contributor.funder | Centro para el Desarrollo Tecnológico Industrial (CDTI) | es |