Fuzzy mathematical approach to pattern recognition
Fuzzy mathematical approach to pattern recognition
On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Fuzzy Sets and Systems - Special memorial volume on mathematical aspects of fuzzy set theory
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Measurement-theoretic justification of connectives in fuzzy set theory
Fuzzy Sets and Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Definition of general aggregation operators through similarity relations
Fuzzy Sets and Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Why fuzzy T-equivalence relations do not resolve the Poincaré paradox, and related issues
Fuzzy Sets and Systems - Theme: Basic notions
Fuzzy Sets and Systems - Implication operators
Generalized OWA Aggregation Operators
Fuzzy Optimization and Decision Making
How good are fuzzy If-Then classifiers?
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this chapter we will study properties and usability of basic many valued structures called t-norms, t-conorms, implications and equivalences in comparison tasks. We will show how these measures can be aggregated with generalized mean and what kind of measures for comparison can be achieved from this procedure. New classes for comparison measures are suggested, which are combination measure based on the use of t-norms and t-conorms and pseudo equivalence measures based on S-type implications. In experimental part of this chapter we will show how some of the comparison measures presented here work in comparison task. For comparison task we use classification. We show by comparison to results that can be achieved through some known public domain classifier results that our classification results are highly competitive.