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

Masahiro Tanaka | Zendy; Hideki Fujiwara | Zendy

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

Author(s) -

Masahiro Tanaka,

Hideki Fujiwara

Publication year - 2000

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2000.p0003

Subject(s) - computer science , categorical variable , artificial neural network , artificial intelligence , pattern recognition (psychology) , perceptron , visualization , associative property , data mining , multilayer perceptron , class (philosophy) , machine learning , mathematics , pure mathematics

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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