A speculative algorithm to extract fuzzy measures from sample data

In Multi-Criteria Decision Making (MCDM), decisions are based on several criteria that are usually conflicting and non-homogenously satisfied. Non-additive (fuzzy) measures along with the Choquet integral can model and aggregate the levels of satisfaction of these criteria by considering their relationships. However, in practice, it is difficult to identify such fuzzy measures. An automated process is necessary and can be used when sample data is available. Several optimization approaches have been proposed to extract fuzzy measures from sample data; for example, genetic algorithms, gradient descent algorithms, and the Bees algorithm. In this article, instead of using the search space as the primary focus of our research, we propose an algorithm that speculates on the value of the objective function before actually arriving to it. In addition, contrary to previous approaches to extracting fuzzy measures, our algorithm guarantees the solution to be global. Our experimental results show that our algorithm improves the performance of previous approaches.