Paper:
Language: English:

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, 1998

Accepted: March 31, 1999

Keywords: Hybridization, Auto-associative neural networks, Multi-layer perceptron, Visualization of data, Data compression

Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.4, No.1 pp. 3-11, 2000

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.

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