Attribute reduction of interval-valued decision system
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In this paper, we establish a dominance relation in interval-valued information systems. With respect to any subset of object set, attribute reduction methods are presented based on substitution of the indiscernibility relation by the dominance relation. Possible and certain decision rules can be derived by possible and uniform distribution functions, respectively. Any part of the decision rules can be identified by these attributes which obtained by the attribute reduction method. The usefulness of each method is illustrated by one example.Keywords:
Variable and attribute
Dominance (genetics)
Decision system
Decision rule
Rule induction method based on rough set theory (RST) has received much attention recently since it may generate a minimal set of rules from the decision system for real-life applications by using of attribute reduction and approximations. The decision system may vary with time, e.g., the variation of objects, attributes and attribute values. The reduction and approximations of the decision system may alter on Attribute Values' Coarsening and Refining (AVCR), a kind of variation of attribute values, which results in the alteration of decision rules simultaneously. This paper aims for dynamic maintenance of decision rules w.r.t. AVCR. The definition of minimal discernibility attribute set is proposed firstly, which aims to improve the efficiency of attribute reduction in RST. Then, principles of updating decision rules in case of AVCR are discussed. Furthermore, the rough set-based methods for updating decision rules in the inconsistent decision system are proposed. The complexity analysis and extensive experiments on UCI data sets have verified the effectiveness and efficiency of the proposed methods.
Decision rule
Decision table
Decision system
Rule induction
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This paper deeply studies the attribute importance represented by rough set.Considering the drawbacks of the existing methods of attribute weight assignment based on rough set and both individual significance degree and the number of values of each attribute in the conditional attributes,it proposes an improved method of attribute weight assignment based on rough set,and it's rationality is proved.A case is used to prove that improved weighting method can solve the existing problem.
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Degree (music)
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As far as a specific rule is concerned, attribute reduction is equivalent to attribute value reduction. Especially for variable precision rough set (VPRS) , any attribute value may be a value reduct, every of them must be checked. Some important information would be ignored if we still first compute attribute reduct and then compute its value reduct just as traditional Rough set theory (RST) in VPRS. This paper, through analyzing the nature of reduction, and the specific meanings of value reduction in VPRS, provide a direct value reduction algorithm.
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Attribute importance represented by rough set is studied deeply. Aiming at the shortcomings of existing methods of attribute weight assignment based on rough set, by considering both the overall significance degree and the individual significance degree of each attribute in the conditional attributes, a new method of attribute weight assignment based on rough set is proposed, which can improve the ability of generalization and interpretation.
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Degree (music)
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As a useful tool for data mining,the rough set theory is widely used in the description of the correlation between attributes of relational database,the reduction of the attribute set,the counting of an attribute importance compared to other attribute importance,the discovery of rules,and so on.This paper discusses the attribute reduction in rough set theory.First,on the basis of analyzing the rough set theory,a detailed description of attribute reduction algorithm based on the discernible matrix is given.Second,as the traditional algorithm has relatively poor efficiency in both time and space when obtaining attribute reduction,the relationship degree,which describes the contribution of one condition attributes to decision attribute,is introduced into rough set to value the importance of attribute.And then a modified algorithm is given by using the relationship degree as heuristic information,Theory analysis and the experimental results show this algorithm costs less time and reduces the number of condition attributes than other algorithms.It establishes a method by which rough set theory is widely used in concrete practice.
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Decision table
Basis (linear algebra)
Degree (music)
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Decision table
Decision system
Basis (linear algebra)
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In this paper,the author discusses the attribute reduction in Rough Sets theory.The paper introduces the concept of the information quantity of decision attribute with relation to given condition attributes,and proves that its changing tendency is monotonously decreasing.The best attribute reduction is the set whose value is the minimum average of relevance of attributes.Then,a new attribute reduction algorithm based on information quantity is developed.An example shows that this algorithm is effective.
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Value (mathematics)
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Rule extraction is an very important and difficult process for an intelligent information system.To deal with the problem,the paper proposed a method based on rough set theory,researched attribute reduction,attribute values reduction and so on.According to the indiscernible relation in rough set,discernible vector and its addition rule were defined to calculate the core attributes and all attributes' importance.The core attributes set was taken as the start point to obtain an attributes reduction set by using the attributes' importance as the heuristic information.Based on the attributes reduction set,attribute value reduction was realized through gradually deleting the redundant attribute values in every rule of the information table depending on the correlation of condition attributes and decision attributes.Finally,a concise rule set was obtained.The illustration and experiment results indicate that the method is effective and efficient for rule extraction.
Decision table
Variable and attribute
Decision rule
Table (database)
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The rough set theory is an effective tool in dealing with uncertain knowledge and incomplete data. This paper proposes a simplified table decision approach base on rough set theory and tourism knowledge, using rough set theory handling large amounts of business information, extract useful rules and generate minimal decision rules through analysis and reasoning. Finally, By analyzing real examples, we proves the feasibility on the combination of rough set theory and the tourism decision support system. Our method effectively solves problems such as the acquisition and understanding of smart marketing decision rules in decision support system.
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Table (database)
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Intelligent development of air-borne external stores management system requires the system to have the ability of intelligent decision.Because of complexity of decision factors,general methods often have large limitations in the application.According to Pawlak's rough set theory,the decision making method of multi-factor rough set in the complete information system was established.Based on the traditional attribution reducing method and decision rule extraction algorithm of rough set,a rule-generation algorithm to generate a minimal set of rule reducing from the complete information system was proposed.Taking a type of aircraft as example,intelligent selection of air-borne weapon was realized by use of module of rough set decision method.The result showed that the proposed method is effective in the process of realization of intelligent decision for external stores management system.
Decision rule
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Realization (probability)
Decision table
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