DocumentCode
1698492
Title
A Bayesian approach to the missing features problem in classification
Author
Lynch, Robert S., Jr. ; Willett, Peter K.
Author_Institution
Naval Underwater Syst. Center, Newport, RI, USA
Volume
4
fYear
1999
fDate
6/21/1905 12:00:00 AM
Firstpage
3663
Abstract
In this paper, the Bayesian data reduction algorithm (BDRA) is extended to classify discrete test observations given the training data contains feature vectors which are missing values. Two methods are used to model missing features in the BDRA, where performance is compared to a neural network using both simulated and real data. In general, it is shown that the BDRA is superior to the neural network
Keywords
Bayes methods; data reduction; pattern classification; BDRA; Bayesian data reduction algorithm; classification; discrete test observation classification; feature vectors; missing features problem; neural network; Bayesian methods; Contracts; Frequency; Hafnium; Neural networks; Probability distribution; Quantization; Random variables; Testing; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1999. Proceedings of the 38th IEEE Conference on
Conference_Location
Phoenix, AZ
ISSN
0191-2216
Print_ISBN
0-7803-5250-5
Type
conf
DOI
10.1109/CDC.1999.827922
Filename
827922
Link To Document