Environmental data stream mining through a case-based stochastic learning approach

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/126638

dc.contributor	Universitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.contributor	Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
dc.contributor.author	Orduña Cabrera, Fernando
dc.contributor.author	Sànchez-Marrè, Miquel
dc.date	2018-08-01
dc.identifier.citation	Orduña Cabrera, F., Sànchez-Marrè, M. Environmental data stream mining through a case-based stochastic learning approach. "Environmental modelling & software", 1 Agost 2018, vol. 106, p. 22-34.
dc.identifier.citation	1364-8152
dc.identifier.citation	10.1016/j.envsoft.2018.01.017
dc.identifier.uri	http://hdl.handle.net/2117/126638
dc.language.iso	eng
dc.publisher	Elsevier
dc.relation	https://www.sciencedirect.com/science/article/pii/S1364815217302657
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
dc.subject	Data science
dc.subject	Data stream mining
dc.subject	Dynamic case learning
dc.subject	Stochastic learning
dc.subject	Case-based reasoning
dc.subject	Air quality detection
dc.subject	Environmental modelling
dc.title	Environmental data stream mining through a case-based stochastic learning approach
dc.type	info:eu-repo/semantics/submittedVersion
dc.type	info:eu-repo/semantics/article
dc.description.abstract	© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.description.abstract	Environmental data stream mining is an open challenge for Data Science. Common methods used are static because they analyze a static set of data, and provide static data-driven models. Environmental systems are dynamic and generate a continuous data stream. Dynamic methods coping with the temporal nature of data must be provided in Data Science. Our proposal is to model each environmental information unit, timely generated, as a new case/experience in a Case-Based Reasoning (CBR) system. This contribution aims to incrementally build and manage a Dynamic Adaptive Case Library (DACL). In this paper, a stochastic method for the learning of new cases and management of prototypes to create and manage the DACL in an incremental way is introduced. This stochastic method works with two main moments. An evaluation of the method has been carried using a data stream of air quality of the city of Obregon, Sonora. México, with good results. In addition, other datasets have been mined to ensure the generality of the approach.
dc.description.abstract	Peer Reviewed

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