Bivariate Generalized Linear Model for Interval-Valued Variables

Current symbolic regression methods visualize problems from an optimization point of view and do not consider the probabilistic aspects related to regression models. In this paper, we present the bivariate generalized linear model (BGLM) proposed by Iwasaki and Tsubaki [5] in the context of interval...

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Bibliographic Details
Main Authors: de A. Lima Neto, E., Cordeiro, G.M., de Carvalho, F.A.T., dos Anjos, U.U., da Costa, A.G.
Format: Conference Proceeding
Language:English
Subjects:
Data analysis
Data visualization
Gaussian processes
Linear regression
Meteorology
Neural networks
Optimization methods
Parameter estimation
Predictive models
Upper bound
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Summary:Current symbolic regression methods visualize problems from an optimization point of view and do not consider the probabilistic aspects related to regression models. In this paper, we present the bivariate generalized linear model (BGLM) proposed by Iwasaki and Tsubaki [5] in the context of interval-valued data sets. Important aspects related to the BGLM that remain open or can be improved will be considered. The performance of this new approach in relation to symbolic regression methods proposed by Billard and Diday [1] and Lima Neto and De Carvalho [7] will be considered through real interval data sets.
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2009.5178711