An experimental evaluation of different methods for handling missing inputs

Author

Fernández, Mercedes ; Hernández, Carlos

Author_Institution

Dept. of Comput., Jaume-1 Univ., Spain

Volume

2

fYear

1998

fDate

4-9 May 1998

Firstpage

1450

Abstract

Presents an empirical comparison among four different methods of handling missing inputs. Eight different classification problems are used for the comparison, and the performance of the methods is obtained for several percentages, 0%, 5%, 10%, 30% and 40%, of missing inputs in the problem. There is one method, which has the best performance in the eight different problems. The method is based on a generalization of backpropagation to interval arithmetic and a codification during training of every missing input by the interval [0, 1]

Keywords

backpropagation; feedforward neural nets; multilayer perceptrons; pattern classification; probability; backpropagation; classification problems; codification; interval arithmetic; missing inputs; Arithmetic; Backpropagation algorithms; Bayesian methods; Computer networks; Data mining; Feedforward neural networks; Medical diagnosis; Multi-layer neural network; Neural networks; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on

Conference_Location

Anchorage, AK

ISSN

1098-7576

Print_ISBN

0-7803-4859-1

Type

conf

DOI

10.1109/IJCNN.1998.685989

Filename

685989