We augment a linear regression procedure by a thruth-functional method in order to identify a highly informative regression line. The idea is to use statistical methods to identify a confidence region for the line and exploit the structure of the sample data falling in this region for identifying the most fitting line. The fitness function is related to the fuzziness of the sampled points as a natural extension of the statistical criterion ruling the identification of the confidence region within the Algorithmic Inference approach. We tested the method on three well known benchmarks.
Granular regression / B. Apolloni, D. Iannizzi, D. Malchiodi, W. Pedrycz - In: Neural nets : 16th italian workshop on neural nets,WIRN2005 and international workshop on natural and artificial immune systems, NAIS 2005, Vietri sul Mare, Italy, june 8-11, 2005 : revised selected papers / [a cura di] B. Apolloni, M. Marinaro, G. Nicosia, R. Tagliaferri. - Berlin : Springer, 2006. - ISBN 9783540331834. - pp. 147-156 (( Intervento presentato al 16th. convegno Italian Workshop on Neural Nets - WIRN 2005 tenutosi a Vietri sul Mare nel 2005.
Granular regression
B. ApolloniPrimo
;D. IannizziSecondo
;D. MalchiodiPenultimo
;
2006
Abstract
We augment a linear regression procedure by a thruth-functional method in order to identify a highly informative regression line. The idea is to use statistical methods to identify a confidence region for the line and exploit the structure of the sample data falling in this region for identifying the most fitting line. The fitness function is related to the fuzziness of the sampled points as a natural extension of the statistical criterion ruling the identification of the confidence region within the Algorithmic Inference approach. We tested the method on three well known benchmarks.
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