Extraction and optimization of classification rules for continuous or mixed-mode data using neural nets

Dianhui Wang; T. S. Dillon

doi:10.1117/12.421090

27 March 2001 Extraction and optimization of classification rules for continuous or mixed-mode data using neural nets

Dianhui Wang, T. S. Dillon

Proceedings Volume 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III; (2001) https://doi.org/10.1117/12.421090
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

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.

Citation Download Citation

Dianhui Wang and T. S. Dillon "Extraction and optimization of classification rules for continuous or mixed-mode data using neural nets", Proc. SPIE 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, (27 March 2001); https://doi.org/10.1117/12.421090

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