Fuzzy rule-based similarity model enables learning from small case bases
Applied Soft Computing
Fast window fusion using fuzzy equivalence relation
Pattern Recognition Letters
A unified view of class-selection with probabilistic classifiers
Pattern Recognition
A method based on shape-similarity for detecting similar opinions in group decision-making
Information Sciences: an International Journal
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Matching pairs of objects is a fundamental operation in data analysis. However, it requires the definition of a similarity measure between objects that are to be matched. The similarity measure may not be adapted to the various properties of each object. Consequently, designing a method to learn a measure of similarity between pairs of objects is an important generic problem in machine learning. In this paper, a general framework of fuzzy logical-based similarity measures based on $\top$-equalities that are derived from residual implication functions is proposed. Then, a model that allows us to learn the parametric similarity measures is introduced. This is achieved by an online learning algorithm with an efficient implication-based loss function. Experiments on real datasets show that the learned measures are efficient at a wide range of scales and achieve better results than existing fuzzy similarity measures. Moreover, the learning algorithm is fast so that it can be used in real-world applications, where computation times are a key feature when one chooses an inference system.