Title :
Hybrid knowledge acquisition by integrating decision trees and neural networks
Author :
Tsujino, Katsuhiko
Author_Institution :
Dept. of Inf. Syst., Mitsubishi Electr. Corp., Hyogo, Japan
Abstract :
Decision tree induction is one of the most effective techniques for acquiring classification knowledge. However, appropriate pre- and post-processors have to be prepared to achieve continuous input/output mapping, because the decision trees basically deal with symbolic knowledge. On the other hand, an artificial neural network is suitable for such a purpose, however, its initial structure is difficult to constitute. The authors´ research goal is to develop a sophisticated knowledge acquisition system integrating decision tree induction for identifying the fundamental structure of the knowledge and neural network generation for realizing an adaptive processor based on the knowledge structure obtained as a decision tree. This paper reports an experimental approach to this goal by constructing a neural network based on the result of decision tree induction from symbolic examples, and analyzing the network to elicit hidden knowledge in numerical examples
Keywords :
decision theory; knowledge acquisition; multilayer perceptrons; pattern classification; trees (mathematics); Neuro-Kaiser; adaptive processor; classification knowledge; decision trees; entropy net; induction; input/output mapping; knowledge acquisition; knowledge elicitation; knowledge structure; multilayer perceptron; neural networks; symbolic knowledge; Algorithm design and analysis; Artificial neural networks; Classification tree analysis; Decision trees; Induction generators; Information systems; Knowledge acquisition; Neural networks; Research and development; Vegetation mapping;
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
DOI :
10.1109/ICNN.1995.487359