@InCollection{DíscolaJúniorCecaFernRibe:2018:OpDaMi,
author = "D{\'{\i}}scola J{\'u}nior, S{\'e}rgio Luisir and Cecatto,
Jos{\'e} Roberto and Fernandes, M{\'a}rcio Merino and Ribeiro,
Marcela Xavier",
title = "An optimized data mining method to support solar flare forecast",
booktitle = "Information technology: new generations, advances in intelligent",
publisher = "Springer",
year = "2018",
editor = "Latifi, S.",
pages = "467--474",
note = "{14th International Conference on Information Technology}",
keywords = "solar flare, forecasting, times series, data mining, feature
selection.",
abstract = "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.",
affiliation = "{Universidade Federal de S{\~a}o Carlos (UFSCar)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal
de S{\~a}o Carlos (UFSCar)} and {Universidade Federal de S{\~a}o
Carlos (UFSCar)}",
doi = "10.1007/978-3-319-54978-1_60",
url = "http://dx.doi.org/10.1007/978-3-319-54978-1_60",
language = "en",
targetfile = "discola_optimized.pdf",
urlaccessdate = "28 mar. 2024"
}