Identification of -fuzzy measures using sampling design and genetic algorithms
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Aggregation of neural classifiers using Choquet integral with respect to a fuzzy measure
2018, NeurocomputingCitation Excerpt :In order to simplify the fuzzy measure, [43] also proposed the λ-fuzzy measure, where n values need to be identified. Since then, aiming to identify these n values, different supervised approaches have been proposed [4,24,27,45]. However, these methods have some shortcomings related to slow convergence and/or computational burden [22].
Disassembly line balancing problem using interdependent weights-based multi-criteria decision making and 2-Optimal algorithm
2018, Journal of Cleaner ProductionChoquet based TOPSIS and TODIM for dynamic and heterogeneous decision making with criteria interaction
2017, Information SciencesCitation Excerpt :Also assuming that the decision maker is able to provide some a priori information about the fuzzy measure, Lee and Leekwang [35] applied a genetic algorithm (GA) to find the fuzzy measure that matches the most with the prior information. There are several other works that use GA to determine the fuzzy measure [8,9], but they all need some a priori information or input-output data. Grabisch [18] used a gradient descent method to determine the fuzzy measure from learning data.
A Short Survey on the Usage of Choquet Integral and its Associated Fuzzy Measure in Multiple Attribute Analysis
2015, Procedia Computer ScienceA novel approach to evaluate software vulnerability prioritization
2013, Journal of Systems and SoftwareParticle swarm optimization for determining fuzzy measures from data
2011, Information SciencesCitation Excerpt :Wang also proposed using the genetic algorithm (GA) to determine fuzzy measures [14,40,51,56–58,63]. Due to the better robustness, GAs are further used by many researchers [9,10,12,27,28,73]. References show that the design of GAs depends on the type of training data.