Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Convergence of self-organizing neural algorithms
Neural Networks
Automated knowledge acquisition
Automated knowledge acquisition
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Effective Data Mining Using Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Feature selection by analyzing class regions approximated byellipsoids
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fuzzy interpretation of discretized intervals
IEEE Transactions on Fuzzy Systems
Knowledge-based fuzzy MLP for classification and rule generation
IEEE Transactions on Neural Networks
A neural-network model for learning domain rules based on its activation function characteristics
IEEE Transactions on Neural Networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
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This paper aims at developing a data mining approach for classification rule representation and automated acquisition from numerical data with continuous attributes. The classification rules are crisp and described by ellipsoidal regions with different attributes for each individual rule. A regularization model trading off misclassification rate, recognition rate and generalization ability is presented and applied to rule refinement. A regularizing data mining algorithm is given, which includes self-organizing map network based clustering techniques, feature selection using breakpoint technique, rule initialization and optimization, classifier structure and usage. An Illustrative example demonstrates the applicability and potential of the proposed techniques for domains with continuous attributes.