A study on hyperbox classifier with domino extension in pattern recognition: Hyperbox driven classifier in pattern recognition
Byoung‐Jun Park, Eun-Hye Jang, Sang‐Hyeob Kim, Myung‐Ae Chung · 2014
In this study, we introduce the development of hyperbox classifier with hierarchical two-level granular structure, namely set (interval) and fuzzy set in dealing with a description of geometry of patterns belonging to a certain category. We take advantage of the capabilities of sets when describing a core structure of classes of patterns in the form of some hyperboxes. Their combinations are referred to as a core structure of the feature space. Next, we refine the geometry of the classifier by bringing forward the concepts of regions of the feature space characterized by fuzzy sets. They are sought as a secondary structure. A series of numeric examples are used to demonstrate the effectiveness of the proposed classifiers.