An analysis about the measure quality of similarity and its applications in machine learning

Abstract

In this paper, a review about the quality of the similarity measure and its applications in machine learning is presented. This measure is analyzed from the perspective of the granular computing. The granular computing allows analyzing the information at different levels of abstraction and from different approaches. The analysis shows that this measure is based on two basic aspects on the universe of objects: the granularity of the information and the principle that, similar problems have similar solutions. Using the measure, a method was formulated to build relations of similarity; these relations and other results have been used in improving machine learning techniques.

Prototypes selection based on similarity relations for classification problems

“…This value was used because better results are obtained. The weights in expression (2) are calculated according to the method proposed in [\6] [17] and the features' comparison function ai (Xi, Yi), which calculates the similarity between the values of objects x and y with respect to the feature instances i, is defined by expression (4), where D; is the domain of feature i:…”