Research and Application of Multi-Criteria Decision Making Method based on Order Relation and Rough Set
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This paper proposed a multi-criteria decision making approach based on extended rough set. The approach combines both rough set theory (RST) model based on dominance relation and variable precision rough set (VPRS) model, and the minimum confidence of rule acts as the variable parameter in VPRS model. First, a pairwise comparison table (PCT) is constructed from original decision table. Then, decision rules are mined from PCT by decision tree technology according to the combined model. Finally, a score function is defined to sort new alternatives and the optical one is selected. Feasibility of the approach is demonstrated by a simple illustrative example.
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Rough sets for knowledge update have been successfully applied in data mining. Methods for incremental updating decision rules based on the indiscernibility, tolerance relation and similarity relations in rough set theory have been previously studied in literature. The characteristic relation-based rough sets approach provides more informative results than the approach employing the indiscernibility, tolerance relations and similarity relations based approach. In this paper, we extend rough sets based on characteristic relations for incrementally updating decision rules. An extensive experimental evaluation validates the efficiency of the proposed approach which may be used to handle a dynamic maintenance of decision rules in data mining.
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The rough set theory is a new method for analyzing and dealing with data. By using this theory, we proposed a risk assessment algorithm based on rough set theory, which was described in detail in this paper. the decision table can be simplified and redundant attributes can be got rid of A method of inference based on the knowledge of rough sets and an example to show how to acquire the rules of new decision making, thus filling the method with a practical and publicizing value are given.
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Decision tables need be first given before rough set models or extensions of rough set model are used to make decision. However, in many uncertain practical decisions, data set can only build knowledge presentation system but impossible to generate decision tables. A hybrid approach of grey clustering and variable precision rough set is proposed in the paper. The basic idea of the hybrid method is as follows. Firstly, a multiple attribute decision table is established by grey fixed weight clustering, and then probabilistic decision rules are derived by applying variable precision rough set. The proposed method is applied to the selection of regional key technology, and the extracted rules can provide decision reference for decision makers.
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Analyze the deficiency of rough set decision-making cost matrix to put forward a kind of multiple fusion rules of cost matrix. Adjust parameter values to make fusion rules in accordance with the application requirement of specific cases. And then establish a multiple integration Decision-Theoretic Rough Sets model. This describes the definition of a decision domain distribution and proposes a method of attribute reduction and decision rule based on differential matrix. The example analysis of the safety evaluation of the Vehicle verifies the feasibility and effectiveness of the method.
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Z. Pawlak discussed the decision rules and relative problems via the combination of Bayesian formula with the model of rough sets, and to establish probability model based on decision theory.By the idea, This paper describes a extracting method of minimum hazard decision rules based on rough set theory. And its applications are illustrated with real examples in bank credit analysis.The method provides a better solving path for non-structure decision problems.
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Rough set theory provides an effective method to reduce attributes and extract knowledge. This paper represents a rough set multi-knowledge extraction algorithm and its formal concept analysis. The proposed algorithm can obtain multi-reducts by using rough set in decision table. The formal concept analysis is used to obtain rules from the main values of the attributes influencing the decision making and these rules build a multi-knowledge. Experimental results show that the proposed multi-knowledge extraction algorithm is efficient.
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A quantitative decision table can be transformed into a qualitative decision one by using the fuzzy set theory. This paper develops the definition of membership function mentioned in the literature, and proposes transforming rules from the quantitative decision table to the qualitative decision table with the properties of membership function. The rules can change an n-dimension quantitative decision table into an n-dimension qualitative decision table instead of a 3n-dimension one. So it greatly decreases afterward computing complexity of rule extraction using rough set theory, while increases the quality of extracted rules.
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