Hyperbox Classifiers for arrhythmia classification

Purpose - This paper sets out to design hyperbox classifiers of high interpretation capabilities. They are based on a collection of hyperboxes - generic and highly interpretable geometric descriptors of data belonging to a certain class. Such hyperboxes directly translate into conditional statements (rules) taking on the well-known format "if feature1 assumes values in [a,b ] and feature2 assumes values in [d,f ] and . . . and featuren assumes values in [w,z ] then class v" where the intervals ([a,b ],. . .[w,z ]) are the respective edges (features) of the corresponding hyperbox. Design/methodology/approach - The proposed design process of hyperboxes consists of two main phases. In the first phase, a collection of "seeds" of the hyperboxes is constructed through data clustering being realized by means of the fuzzy C-means algorithm. During the second phase, the hyperboxes are "grown" (expanded) by applying mechanisms of genetic optimization (and genetic algorithm, in particular). Findings - It is demonstrated how the underlying geometry of the hyperboxes supports an immediate interpretation of arrhythmia data by linking the ranges of the features (parameters of the ECG signal) forming the edges of the hyperboxes with the two classes of the signals (normal - abnormal). A collection of comprehensive experiments offers an interesting insight into the geometry of the individual categories of the ECG signals and discusses how the resulting hyperbox classifiers link their geometric properties with the obtained classification rates. Research limitations/implications - The structure of the classifier is essential to enhance interpretation capabilities of the architecture and generate a collection of "if-then" classification rules. Originality/value - The study addresses an issue of design of highly interpretable, granular classifiers with the use of the technology of computational intelligence and evolutionary optimization, in particular.