@InProceedings{AnochiCamp:2015:DiReUs,
author = "Anochi, Juliana Aparecida and Campos Velho, Haroldo Fraga de",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)}",
title = "Dimensionality reduction using rough set approach for climate
prediction",
booktitle = "Anais...",
year = "2015",
pages = "181--185",
organization = "International Conference on Applied Computing, 12.",
publisher = "IADIS: International Association for Development of the
Information Society",
keywords = "Data mining, Rough sets theory, Optimal neural network, MPMaynooth
(Irlanda) Lisboa (Portugal)CA: multi-particle collision algorithm,
Climate prediction.",
abstract = "In this article, a data mining method to variables selection for
climate prediction is presented. The data were processed by Rough
Set Theory to extract relevant information to perform the seasonal
climate prediction by neural network for the South of Brazil, with
a reduced data set. The neural network was self-configured by MPCA
metaheuristic. Two experiments were conducted with neural network:
complete meteorological input variables, and reduced data set
extract from the rough set theory.",
conference-location = "Maynooth",
conference-year = "24-26 Oct.",
isbn = "9789898533456",
label = "lattes: 5142426481528206 2 AnochiCamp:2015:DiReUs",
language = "en",
targetfile = "1_anochi.pdf",
urlaccessdate = "27 abr. 2024"
}