Methodology for Generation and Selection of Fuzzy Classifiers of Mixed-Type Data
DOI: 10.21293/1818-0442-2025-28-2-88-95
DOI: 10.21293/1818-0442-2025-28-2-88-95
Abstract: A description of the method for constructing fuzzy classifiers of mixed-type data, their multi-criteria assessment and selection based on optimality principles is given. The method consists of the following main sections: 1) three-stage construction of a set of fuzzy classifiers of mixed data using Grasshopper Optimization Algorithm; 2) ranking of the obtained classifiers based on three criteria: classification error, number of features, number of rules; 3) normalization of ranks; 4) formed Pareto sets of classifiers; 5) selection of a fuzzy classifier based on optimality principles.
Keywords: clustering, fuzzy classifier, mixed data, Grasshopper Optimization Algorithm
Funding: This work was supported by the Russian Science Foundation, grant no. 24-21-00168 (https://rscf.ru/project/24-21-00168/).
For citation:
Ostapenko R. O., Hodashinskiy I. A. Methodology for Generation and Selection of Fuzzy Classifiers of Mixed-Type Data. Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki, 2025, vol. 28, no. 2, pp. 88–95. DOI: 10.21293/1818-0442-2025-28-2-88-95
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