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Proceedings Paper

Memory- and neural-network-based expert system
Author(s): Chung Kwan Shin; Sang Chan Park
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Paper Abstract

We suggest a hybrid expert system of memory and neural network-based learning. Neural network (NN) and memory-based reasoning (MBR), have common advantages over other learning strategies. NN and MBR can be directly applied to the classification and regression problem, without additional transform mechanisms. They also have strength in learning the dynamic behavior of the system over a period in time. Unfortunately, they have shortcomings. The knowledge representation of NN is unreadable to human beings, and this 'black box' property restricts the application of NN to areas which need proper explanation, as well as precise predictions. On the other hand, MBR suffers from the feature weighting problem. When MBR measures the distance between cases, some features should be treated more importantly than others. Although previous researchers provide several feature weighting mechanisms to overcome the difficulty, those methods were mainly applicable only to the classification problem. In our hybrid system of NN and MBR, the feature weight set calculated from the trained neural network plays the core role in connecting both learning strategies. Moreover, the explanation on prediction can be given by presenting the most similar cases from the case base. In this paper, we present the basic idea of the hybrid system and preliminary experimental results. Experimental results show that the hybrid system predicts the yield with relatively high accuracy and has a distinct potential in feature selection.

Paper Details

Date Published: 25 February 1999
PDF: 9 pages
Proc. SPIE 3695, Data Mining and Knowledge Discovery: Theory, Tools, and Technology, (25 February 1999); doi: 10.1117/12.339993
Show Author Affiliations
Chung Kwan Shin, Korea Advanced Institute of Science and Technology (South Korea)
Sang Chan Park, Korea Advanced Institute of Science and Technology (South Korea)

Published in SPIE Proceedings Vol. 3695:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology
Belur V. Dasarathy, Editor(s)

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