dc.contributor.authorEnríquez, J. G.
dc.contributor.authorMartínez-Rojas, A.
dc.contributor.authorLizcano, David
dc.contributor.authorJiménez-Ramírez, A.
dc.date.accessioned2020-10-22T12:24:21Z
dc.date.available2020-10-22T12:24:21Z
dc.date.issued2020-06-03
dc.identifier.urihttp://hdl.handle.net/20.500.12226/515
dc.description.abstractNowadays, 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, and (3) a traceability links maintenance. 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.language.isoenes
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleA Unified Model Representation of Machine Learning Knowledgees
dc.typearticlees
dc.description.course2019-20es
dc.identifier.doihttps://doi.org/10.13052/jwe1540-9589.1929
dc.identifier.essn1544-5976
dc.issue.number2es
dc.journal.titleJournal of Web Engineeringes
dc.page.initial319es
dc.page.final340es
dc.publisher.facultyEscuela de Ciencias Técnicas e Ingenieríaes
dc.publisher.group(GI-14/4) Ingeniería y Gestión del Conocimientoes
dc.rights.accessRightsopenAccesses
dc.subject.keywordMachine Learninges
dc.subject.keywordAutomated Machine Learninges
dc.subject.keywordKnowledge Representationes
dc.subject.keywordModel-Driven Engineeringes
dc.volume.number19es


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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