Neural networks for soft decision making

Citation
H. Ishibuchi et M. Nii, Neural networks for soft decision making, FUZ SET SYS, 115(1), 2000, pp. 121-140
Citations number
24
Language
INGLESE
art.tipo
Article
Categorie Soggetti
Engineering Mathematics
Journal title
FUZZY SETS AND SYSTEMS
ISSN journal
0165-0114 → ACNP
Volume
115
Issue
1
Year of publication
2000
Pages
121 - 140
Database
ISI
SICI code
0165-0114(20001001)115:1<121:NNFSDM>2.0.ZU;2-P
Abstract
This paper discusses various techniques for soft decision making by neural networks. Decision making problems are described as choosing an action from possible alternatives using available information. In the context of soft decision making, a single action is not always chosen. When it is difficult to choose a single action based on available information, the decision is withheld or a set of promising actions is presented to human users. The abi lity to handle uncertain information is also required in soft decision maki ng. In this paper. we handle decision making as a classification problem wh ere an input pattern is classified as one of given classes. Class labels in the classification problem correspond to alternative actions in decision m aking. In this paper, neural networks are used as classification systems, w hich eventually could be implemented as a part of decision making systems. First we focus on soft decision making by trained neural networks. We assum e that the learning of a neural network has already been completed. When a new pattern cannot be classified as a single class with high certainty by t he trained neural network, the classification of such a new pattern is reje cted. After briefly describing rejection methods based on crisp outputs fro m the trained neural network, we propose an interval-arithmetic-based rejec tion method with interval input vectors, and extend it to the case of fuzzy input vectors. Next we describe the learning of neural networks for possib ility analysis. The aim of possibility analysis is to present a set of poss ible classes of a new pattern to human users. Then we describe the learning of neural networks from training patterns with uncertainty. Such training patterns are denoted by interval vectors and fuzzy vectors. Finally we exam ine the performance of various soft decision making methods described in th is paper by computer simulations on commonly used data sets in the literatu re. (C) 2000 Elsevier Science B.V. All rights reserved.