|
Title:
|
DIMENSIONALITY REDUCTION USING ROUGH SET APPROACH FOR CLIMATE PREDICTION |
|
Author(s):
|
Juliana Aparecida Anochi, Haroldo Fraga de Campos Velho |
|
ISBN:
|
978-989-8533-45-6 |
|
Editors:
|
Hans Weghorn |
|
Year:
|
2015 |
|
Edition:
|
Single |
|
Keywords:
|
Data mining, rough sets, optimal neural network, multiple particle collision algorithm, climate prediction. |
|
Type:
|
Short Paper |
|
First Page:
|
181 |
|
Last Page:
|
185 |
|
Language:
|
English |
|
Cover:
|
|
|
Full Contents:
|
click to dowload 
|
|
Paper 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. |
|
|
|
|
|
|
