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Concept drift, given the huge volume of high-speed data streams, requires traditional machine learning models to be self-adaptive. Techniques to handle drift are especially needed in regression cases for a wide range of applications in the real world. There is, however, a shortage of research on drift adaptation for regression cases in the literature. One of the main obstacles to further research...
Evolving fuzzy classifiers (EFCs) have achieved immense success in dealing with nonstationary data streams because of their flexible characteristics. Nonetheless, most real-world data streams feature highly uncertain characteristics, which cannot be handled by the type-1 EFC. A novel interval type-2 fuzzy classifier, namely evolving type-2 classifier (eT2Class), is proposed in this paper, which constructs...
Product prototype evaluation is an important phase in new product development (NPD). Such evaluation often requires multiple criteria that are within a hierarchy and a group of evaluators. The evaluation process and these evaluation criteria often involve uncertain and fuzzy data in the weights of these criteria and the judgments of these evaluators. To evaluate nonwoven cosmetic product prototypes,...
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