Constructing a classifier with patterns

The goal of pattern extraction is to find some regularities among the objects included in a data base. When these objects are labeled the patterns can be used for the classification of unseen objects. Most of the algorithms for pattern extraction are based on finding the most frequent structures (or patterns). However, classifiers formed by the set of the most frequent patterns are not necessarily good classifiers since frequent patterns are, most of times, presents in objects of all the classes. In the current paper we propose a method to find patterns that, with a simple post-process, will form an appropriate model for classification. This method resembles to the procedure to grow decision trees, but relevant feature extraction is performed using JADE, a method based on the minimization of the distance between two indistinguishability relations.

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