%0 Book Section
%@nexthigherunit 8JMKD3MGPCW/3ETR8EH
%3 discola_optimized.pdf
%4 sid.inpe.br/mtc-m21c/2018/07.03.15.34
%A Díscola Júnior, Sérgio Luisir,
%A Cecatto, José Roberto,
%A Fernandes, Márcio Merino,
%A Ribeiro, Marcela Xavier,
%@secondarytype PRE LI
%B Information technology: new generations, advances in intelligent
%D 2018
%E Latifi, S.,
%@secondarykey INPE--/
%I Springer
%K solar flare, forecasting, times series, data mining, feature selection.
%O 14th International Conference on Information Technology
%P 467-474
%T An optimized data mining method to support solar flare forecast
%X Historical Solar X-rays time series are employed to track solar activity and solar flares. High level of X-rays released during Solar Flares can interfere in telecommunication equipment operation. In this sense, it is important the development of computational methods to forecast Solar Flares analyzing the X-ray emissions. In this work, historical Solar X-rays time series sequences are employed to predict future Solar Flares using traditional classification algorithms. However, for large data sequences, the classification algorithms face the problem of dimensionality curse, where the algorithms performance and accuracy degrade with the increase in the sequence size. To deal with this problem, we proposed a method that employs feature selection to determine which time instants of a sequence should be considered by the mining process, reducing the processing time and increasing the accuracy of the mining process. Moreover, the proposed method also determines which are the antecedent time instants that most affect a future Solar Flare.
%@area CEA
%@electronicmailaddress sergio.discola@dc.ufscar.br
%@electronicmailaddress jr.cecatto@inpe.br
%@documentstage not transferred
%@group
%@group DIDAS-CGCEA-INPE-MCTIC-GOV-BR
%@dissemination BNDEPOSITOLEGAL
%@usergroup simone
%@resumeid
%@resumeid 8JMKD3MGP5W/3C9JHJB
%@affiliation Universidade Federal de São Carlos (UFSCar)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Universidade Federal de São Carlos (UFSCar)
%@affiliation Universidade Federal de São Carlos (UFSCar)
%@versiontype publisher
%@holdercode {isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}
%@doi 10.1007/978-3-319-54978-1_60
%2 sid.inpe.br/mtc-m21c/2018/07.03.15.34.42