Comparison measures and their usage with examples

Abstract In this article we will review theory of comparison which is a very crucial when decisions have to be done. We will present Minkowski distance-based operator, non-metric-based pseudo similarity based operator and combined t-norm and t-conorm operators. We will study these measures for comparison vs. to the measures that are used generally for comparison that are min, max, Euclidean distance and exponent. Practical part of this article show that presented comparison operators work well in classification example and in expert system example. As a classification example we will use classification done for Image Segmentation data and expert system example is about defining athlete’s aerobic and anaerobic thresholds. Classification results are better than decision tree-, KNN- and SVM-classifiers give for Image Segmentation. Classification accuracy given by Shweizer & Sklar - Łukasiewicz - equivalence was 89.70%, while best result from the classifiers selected for comparison was given by decision medium tree classifier with 72.00% accuracy. In expert system our comparison measure-based method gives similar estimations as given by sport medicine experts.