Visualization of Categorical Data by Hybridization of Two Types of Neural Networks

Masahiro Tanaka; Hideki Fujiwara

doi:10.20965/jaciii.2000.p0003

single-jc.php

« previous

JACIII Vol.4 No.1 pp. 3-11

doi: 10.20965/jaciii.2000.p0003

(2000)

Paper:

Views over last 60 days: 342

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

Published:

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:

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

Visualization of Categorical Data by Hybridization of Two Types of Neural Networks

Masahiro Tanaka* and Hideki Fujiwara**

Masahiro Tanaka^* and Hideki Fujiwara^**