Extraction of classification rules characterized by ellipsoidal regions using soft-computing techniques
This article presents a soft-computing-based data mining technique that addresses methodology aspects on extracting classification rules characterized by ellipsoidal regions in feature space. Self-organizing mapping and statistical techniques are employed to initialize the rules. A regularization model embedding some information on recognition rate and generalization ability is presented for refining the initial rules. Rule optimization is implemented for each individual rule using an evolutionary strategy. To generate rules for patterns with low probability of occurrence but considerable conceptual importance, a multilayer structure of rule generation and use is proposed. Simulation results are carried out by three benchmark data sets, and compared with other data mining tools and classifiers, such as decision trees, BRAINNE (Building Representations of Artificial Intelligence (AI) using Neural Networks), support vector machine, and neural networks. Our technique demonstrates its power and potential for real-world applications.
data mining self-organizing mapping regularization evolutionary strategy multilayer classification systems rule extraction