Fuzzy fusion for skin detection
Fuzzy Sets and Systems
Fuzzy MCDM for evaluating the e-commerce strategy
International Journal of Computer Applications in Technology
Fuzzy measure on vehicle routing problem of hospital materials
Expert Systems with Applications: An International Journal
Applying fuzzy measures and nonlinear integrals in data mining
Fuzzy Sets and Systems
International Journal of Approximate Reasoning
Expert Systems with Applications: An International Journal
Particle swarm optimization for determining fuzzy measures from data
Information Sciences: an International Journal
Feature interaction in subspace clustering using the Choquet integral
Pattern Recognition
Combining grey relation and TOPSIS concepts for selecting an expatriate host country
Mathematical and Computer Modelling: An International Journal
A fuzzy integral-based model for supplier evaluation and improvement
Information Sciences: an International Journal
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This study develops an identification procedure for general fuzzy measures using genetic algorithms. In view of the difficulty in data collection in practice, the amount of input data is simplified through a sampling procedure concerning attribute subsets, and the corresponding detail design is adapted to the partial information acquired by the procedure. A specially designed genetic algorithm is proposed for better identification, including the development of the initialization procedure, fitness function, and three genetic operations. To show the applicability of the proposed method, this study simulates a set of experimental data that are representative of several typical classes. The experimental analysis indicates that using genetic algorithms to determine general fuzzy measures can obtain satisfactory results under the framework of partial information