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@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"
}


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