Title :
An experimental evaluation of different methods for handling missing inputs
Author :
Fernández, Mercedes ; Hernández, Carlos
Author_Institution :
Dept. of Comput., Jaume-1 Univ., Spain
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;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.685989
