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dc.contributor.authorFong, Simon James
dc.contributor.authorLi, Gloria
dc.contributor.authorDey, Nilanjan
dc.contributor.authorGonzález-Crespo, Rubén (1)
dc.contributor.authorHerrera-Viedma, Enrique
dc.date2020-03
dc.date.accessioned2020-06-08T07:25:01Z
dc.date.available2020-06-08T07:25:01Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/10148
dc.description.abstractEpidemic is a rapid and wide spread of infectious disease threatening many lives and economy damages. It is important to fore-tell the epidemic lifetime so to decide on timely and remedic actions. These measures include closing borders, schools, suspending community services and commuters. Resuming such curfews depends on the momentum of the outbreak and its rate of decay. Being able to accurately forecast the fate of an epidemic is an extremely important but difficult task. Due to limited knowledge of the novel disease, the high uncertainty involved and the complex societal-political factors that influence the widespread of the new virus, any forecast is anything but reliable. Another factor is the insufficient amount of available data. Data samples are often scarce when an epidemic just started. With only few training samples on hand, finding a forecasting model which offers forecast at the best efforts is a big challenge in machine learning. In the past, three popular methods have been proposed, they include 1) augmenting the existing little data, 2) using a panel selection to pick the best forecasting model from several models, and 3) fine-tuning the parameters of an individual forecasting model for the highest possible accuracy. In this paper, a methodology that embraces these three virtues of data mining from a small dataset is proposed. An experiment that is based on the recent coronavirus outbreak originated from Wuhan is conducted by applying this methodology. It is shown that an optimized forecasting model that is constructed from a new algorithm, namely polynomial neural network with corrective feedback (PNN+cf) is able to make a forecast that has relatively the lowest prediction error. The results showcase that the newly proposed methodology and PNN+cf are useful in generating acceptable forecast upon the critical time of disease outbreak when the samples are far from abundant.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligencees_ES
dc.relation.ispartofseries;vol. 6, nº 1
dc.relation.urihttps://www.ijimai.org/journal/bibcite/reference/2752es_ES
dc.rightsopenAccesses_ES
dc.subjectdata mininges_ES
dc.subjectmethodes_ES
dc.subjectmachine learninges_ES
dc.subjectforecastinges_ES
dc.subjectpredictiones_ES
dc.subjectepidemices_ES
dc.subjectJCRes_ES
dc.titleFinding an accurate early forecasting model from small dataset: A case of 2019-nCoV novel coronavirus outbreakes_ES
dc.typeArticulo Revista Indexadaes_ES
reunir.tag~ARIes_ES
dc.identifier.doihttps://doi.org/10.9781/ijimai.2020.02.002


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