Fuzzy decision tree induction algorithms require the fuzzy quantization of the input variables. This paper demonstrates that supervised fuzzy clustering combined with similarity-based rule-simplification algorithms is an effective tool to obtain the fuzzy quantization of the input variables, so the synergistic combination of supervised fuzzy clustering and fuzzy decision tree induction can be e...

in structural time seriesregression models, binary (classic) dummy variables are used to quantify the effects of economic structural breaks, changes or qualitative variables. the binary dummy variable can be challenged using this dummy variable in which 0 or 1 represent absence or presence of structural breaks(or changes), but presence of the structural breaks(or changes) may not have uniform e...

This paper provides a Fuzzy based decision support system (DSS) for risk analysis in E-tourism ( Electronic Tourism ) investment. In general term, E-tourism is the use of information and communication technology (ICT) in tourism which may allow operating tourism in least variable cost, least time and increased work efficiency. It is worth noting that there are many factors that affect the deve...

multi attribute decision making methods are considered as one of the most useful methods for solving ranking problems. in some decision making problems, while the alternatives for corresponding criteria are compared in a pairwise comparison manner, if the criteria are inherently fuzzy, debates will arise in ranking alternatives due to the closeness of the values of the criteria. in this researc...

This paper develops Fuzzy inventory model to determine the relevant profit maximizing decision variable values. The model proposed is based on individual profits of vendor and buyer and joint total profit of them which finds out optimal ordering quantity, selling price and shipment policies. Shortages are allowed for both of them. Production rate, ordering quantity, shortage and holding cost of...

risk response planning is one of the main phases in the project risk management and has major impacts on the success of a large-scale project. since projects are unique, and risks are dynamic through the life of the projects, it is necessary to formulate responses of the important risks. the conventional approaches tend to be less effective in dealing with the impreciseness of risk response pl...

OBJECTIVE The aim of this paper is to present a novel fuzzy classification framework for the automatic extraction of fuzzy rules from labeled numerical data, for the development of efficient medical diagnosis systems. METHODS AND MATERIALS The proposed methodology focuses on the accuracy and interpretability of the generated knowledge that is produced by an iterative, flexible and meaningful ...

This paper incorporates fuzzy random variables with a portfolio selection problem based on the single index model. The rate of return on each investment can be represented with a fuzzy random variable. A novel decision-making model based both on possibilistic programming and on stochastic programming. It is shown that the formulated problem is transformed into the deterministic equivalent nonli...

A decision is often made in a group and decision makers often utilize fuzzy judgments in attempting to reach an optimal solution. In order to deal with the fuzziness of decision makers' judgments in solutions, this paper proposes a fuzzy group decision-making method for multi-objective decision problems. The method allows group members to express their fuzzy preferences for decision objectives,...