Topic
Fuzzy number
About: Fuzzy number is a research topic. Over the lifetime, 35606 publications have been published within this topic receiving 972544 citations.
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TL;DR: In this paper, a technology selection algorithm to quantify both tangible and intangible benefits in fuzzy environment is presented, where decision-makers are asked to express their opinions on comparative importance of various factors in linguistic terms rather than exact numerical values.
193 citations
TL;DR: In this article, the problem of ranking a set of alternatives, represented by triangular fuzzy numbers, in decision-making situations is dealt with, and three new methods are proposed, and a notion of preference between alternatives is suggested.
Abstract: This paper deals with the problem of ranking a set of alternatives, represented by triangular fuzzy numbers, in decision-making situations. Three new methods are proposed, and a notion of preference between alternatives is suggested. A comparison with other methods is provided in the concluding table. © 1998 John Wiley & Sons, Inc.
193 citations
01 Mar 2003
TL;DR: An iterative approach for developing fuzzy classifiers is proposed and the initial model is derived from the data and subsequently, feature selection and rule-base simplification are applied to reduce the model, while a genetic algorithm is used for parameter optimization.
Abstract: The automatic design of fuzzy rule-based classification systems based on labeled data is considered. It is recognized that both classification performance and interpretability are of major importance and effort is made to keep the resulting rule bases small and comprehensible. For this purpose, an iterative approach for developing fuzzy classifiers is proposed. The initial model is derived from the data and subsequently, feature selection and rule-base simplification are applied to reduce the model, while a genetic algorithm is used for parameter optimization. An application to the Wine data classification problem is shown.
193 citations
TL;DR: A Fuzzy Genetic Algorithm is developed to generate fuzzy classification rules using several techniques such as multi-value logic coding, composite fitness function, viability check, and rule extraction to improve the efficiency and the effectiveness of the algorithm.
192 citations
TL;DR: Fuzzy concepts are shown to be very useful and easy to work with in this decision-aid problem where it seems interesting to use fuzzy sets.
192 citations