A feature selection method for multi-class-set classification

A versatile technique for set-feature selection from class features without any prior knowledge for multi-class-set classification is presented. A class set is a group of classes in which the patterns represented with class features can be classified with a existing classifier. The features used to classify patterns between classes within a class set are referred to as class features and the ones used to classify patterns between class sets as set features. A set-feature set is produced from class-feature sets under the criterion of minimizing the encounter zones between class sets in set-feature space. The performance of this technique was illustrated with an experiment on the understanding of circuit diagrams