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dc.contributor.authorMosqueira-Rey, E.
dc.contributor.authorAlonso Ríos, David
dc.contributor.authorBaamonde-Lozano, Andrés
dc.date.accessioned2022-01-13T19:41:58Z
dc.date.available2022-01-13T19:41:58Z
dc.date.issued2021
dc.identifier.citationMOSQUEIRA-REY, Eduardo; ALONSO-RÍOS, David; BAAMONDE-LOZANO, Andrés. Integrating Iterative Machine Teaching and Active Learning into the Machine Learning Loop. Procedia Computer Science, 2021, vol. 192, p. 553-562. https://doi.org/10.1016/j.procs.2021.08.057es_ES
dc.identifier.urihttp://hdl.handle.net/2183/29381
dc.description.abstract[Abstract] Scholars and practitioners are defining new types of interactions between humans and machine learning algorithms that we can group under the umbrella term of Human-in-the-Loop Machine Learning (HITL-ML). This paper is focused on implementing two approaches to this topic—Iterative Machine Teaching (iMT) and Active Learning (AL)—and analyzing how to integrate them in the learning loop. iMT is a variation of Machine Teaching in which a machine acts as a teacher that tries to transfer knowledge to a machine learning model. The focus of the problem in iMT is how to obtain the optimal training set given a machine learning algorithm and a target model. The idea is to learn a target concept with a minimal number of iterations with the smallest dataset. Active Learning, in contrast, is a specialized type of supervised learning in which humans are incorporated in the loop to act as oracles that analyze unlabeled data. AL allows us to achieve greater accuracy with less data and less training. Our proposal to incorporate iMT and AL into the machine learning loop is to use iMT as a technique to obtain the “Minimum Viable Data (MVD)” for training a learning model, that is, a dataset that allows us to increase speed and reduce complexity in the learning process by allowing to build early prototypes. Next, we will use AL to refine this first prototype by adding new data in an iterative and incremental way. We carried out several experiments to test the feasibility of our proposed approach. They show that the algorithms trained with the teachers converge faster than those that have been trained in a conventional way. Also, AL helps the model to avoid getting stuck and to keep improving after the first few iterations. The two approaches investigated in this paper can be considered complementary, as they correspond to different stages in the learning process.es_ES
dc.description.sponsorshipThis work has been supported by the State Research Agency of the Spanish Government (grant PID2019-107194GB-I00 / AEI / 10.13039/501100011033) and by the Xunta de Galicia (grant ED431C 2018/34) with the European Union ERDF funds. We wish to acknowledge the support received from the Centro de Investigaci ́on de Galicia “CITIC”, funded by Xunta de Galicia and the European Union (European Regional Development Fund- Galicia 2014-2020 Program, grant ED431G 2019/01)es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2018/34es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107194GB-I00/ES/ANALISIS DE ESTRATEGIAS PARA INCORPORAR HUMANOS AL PROCESO DE APRENDIZAJE AUTOMATICO Y SU APLICACION A LA INVESTIGACION DEL CANCER PANCREATICO/
dc.relation.urihttps://doi.org/10.1016/j.procs.2021.08.057es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacionales_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectIterative Machine Teachinges_ES
dc.subjectActive learninges_ES
dc.subjectMachine learninges_ES
dc.subjectHuman-in-the-Loop Machine Learninges_ES
dc.titleIntegrating Iterative Machine Teaching and Active Learning into the Machine Learning Loopes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleProcedia Computer Sciencees_ES
UDC.volume192es_ES
UDC.startPage553es_ES
UDC.endPage562es_ES
dc.identifier.doi10.1016/j.procs.2021.08.057
UDC.conferenceTitle25th International Conference on Knowledge-Based and Intelligent Information & Engineering Systemses_ES


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