Extraction and optimization of classification rules for continuous or mixed-mode data using neural nets
Dianhui Wang, Tharam Singh Dillon · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
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.