Learning Weights from Observations for Hierarchical Fuzzy Signatures

Abstract

Our earlier work proposed and discussed the issues of a method for obtaining weights, which are associated with the weighted relevance aggregation method, for hierarchical fuzzy signatures from real world data. This method also handled the non-differentiability of conventional max-min aggregation functions, using a mathematically proved method in the literature. This paper applies the proposed method to extract weights for two real world hierarchical fuzzy signatures structures namely Salary Selection and SARS Patient Classification. Based on the results of the experiments for weights extraction with SARS and non-SARS patients data we show that our weights learning method for hierarchical fuzzy signatures not only performs better in separating SARS and non-SARS patients, but also separating non-SARS data into further significantly distinguished and ordered available categories.