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Environmental data stream mining through a case-based stochastic learning approach
Authors
Fernando Orduña Cabrera
Miquel Sànchez-Marrè
Publication date
1 January 2018
Publisher
'Elsevier BV'
Doi
Cite
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/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.Peer ReviewedPostprint (author's final draft
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Last time updated on 02/02/2019
UPCommons
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:upcommons.upc.edu:2117/126...
Last time updated on 17/04/2020