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Visualization of Categorical Data by Hybridization of Two Types of Neural Networks
Masahiro Tanaka* and Hideki Fujiwara**
*Faculty of Science, Konan University 8-9-1 Okamoto, Higashinada-ku, Kobe 658-8501, Japan
**Sakaide Plant, Mitsubishi Chemical Corporation Bannosu-cho, Sakaide, Kagawa 762-8510, Japan
Received:October 1, 1998Accepted:March 31, 1999Published:January 20, 2000
Keywords:Hybridization, Auto-associative neural networks, Multi-layer perceptron, Visualization of data, Data compression
Abstract
The sandglass neural network is often used for nonlinear auto-association, where the principal information can be extracted by picking up the values of the middle layer. However, the boundary of the classes on this 2-1) surface tends to be complicated because no class information is used. In this paper, the hybridization of auto-associative network and the multi-layer perceptron for classification is proposed. The usefulness of this method is demonstrated by using clinical data.
Cite this article as:M. Tanaka and H. Fujiwara, “Visualization of Categorical Data by Hybridization of Two Types of Neural Networks,” J. Adv. Comput. Intell. Intell. Inform., Vol.4 No.1, pp. 3-11, 2000.Data files:
