Title :
Interval-based neural networks for soft decisions
Author :
Nava, Patricia A.
Author_Institution :
Dept. of Electr. & Comput. Eng., Texas Univ., El Paso, TX, USA
Abstract :
The performance of neural networks, when the training data is limited, can be improved by incorporation of interval techniques. These techniques improve performance by introducing the ability to classify imprecise data. Performance can be further improved by incorporating the ability to make soft decisions. Soft decisions differ from hard decisions by allowing the decision-making system the option of deferring to a human. This decision-rejection option has the effect of reducing the error rate of the decision-making system. The paper discusses three distinct techniques for making soft decisions and the performance of the interval-based neural network that utilizes these techniques
Keywords :
decision support systems; feedforward neural nets; fuzzy neural nets; fuzzy set theory; pattern classification; uncertainty handling; ANNs; artificial neural networks; decision-making system; decision-rejection option; error rate reduction; fuzzy systems; hard decisions; imprecise data classification; interval computation; interval techniques; interval-based neural networks; soft decisions; training data; Artificial neural networks; Character recognition; Computer networks; Decision making; Error analysis; Fuzzy systems; Humans; Neural networks; Testing; Training data;
Conference_Titel :
Systems, Man, and Cybernetics, 2001 IEEE International Conference on
Conference_Location :
Tucson, AZ
Print_ISBN :
0-7803-7087-2
DOI :
10.1109/ICSMC.2001.972022