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Proceedings Paper

Extraction and optimization of classification rules for continuous or mixed-mode data using neural nets
Author(s): Dianhui Wang; T. S. Dillon
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Paper Abstract

Extracting and optimizing rules from continuous or mixed- mode data directly for pattern classification problems is a challenging problem. Self-organizing neural-nets are employed to initialize the rules. A regularization model which trades off misclassification rate, recognition rate and generalization ability is first presented for refining the initial rules. To generate rules for patterns with lower probability density but considerable conceptual importance, an approach to iteratively resolving the clustering part for a filtered set of data is used. The methodology is evaluated using Iris data and demonstrates the effectiveness of technique.

Paper Details

Date Published: 27 March 2001
PDF: 8 pages
Proc. SPIE 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, (27 March 2001); doi: 10.1117/12.421090
Show Author Affiliations
Dianhui Wang, Hong Kong Polytechnic Univ. (Australia)
T. S. Dillon, Hong Kong Polytechnic Univ. (Hong Kong)


Published in SPIE Proceedings Vol. 4384:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology III
Belur V. Dasarathy, Editor(s)

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