A reduction algorithm based on trie tree of inconsistent system
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Abstract:
Attribute reduction is one of the vital research contents of Rough Sets. A method of calculating positive region based on the trie tree has been proposed in the paper. And the time complexity is O (∣ C ∥ U ∣). A complete attribute reduction algorithm has been designed according to the method of calculating positive region. The experiment results show that the reduction algorithm is effective, and it is applicable to the reduction of mass data.Keywords:
Trie
Tree (set theory)
Data Reduction
One of the main obstacles facing current data mining techniques is attribute reduction. This paper discusses the basic concepts of rough set, and studies two rough approximations operators under neighborhood systems. An attribute reduction method based on rough set theory and neighborhood systems is presented. The experimental results show that the method of attributes reduction with rough sets and neighborhood system is feasible and valid.
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Attribute reduction is a popular topic in rough set theory; however, object reduction is not considered popularly. In this paper, from a viewpoint of computing all relative reducts, we introduce a concept of object reduction that reduces the number of objects as long as possible with keeping the results of attribute reduction in the original decision table.
Decision table
Table (database)
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Rough set theory is a new mathematical tool to deal with fuzzy and uncertain knowledge.Attribute reduction is one of the most important steps of rough set theory.This paper introduces an improved method to deal with the reduction of attribute based on FPGA by applying genetic algorithm,which makes use of attribute signification to get the core attributes and applies the result to the reduction.Compared with the original method,the improved method can reduce the iteration steps and accelerate the speed of attribute reduction.
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Attribute reduction is one of the key topics in the rough set theory field,the authors discuss the attribute reduction in rough sets theory.Firstly,the basic attribute reduction algorithm in discernibility matrix is introduced.Then,according to the algorithm of rough sets-GENRED,a new attribute reduction algorithm based on rough sets,is proposed.And the new algorithm can meet the requirement of the attribute reduction and improve the operating efficiency.
Matrix (chemical analysis)
Decision table
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In view of the deficiencies of attribute reduction in classic rough set and to further expand the application of rough set attribute reduction,Render an approximate attribute reduction of rough set and a new definition of the importance of attributes.Theory proves that approximate attribute reduction is an extension of the traditional attribute reduction.With the knowledge classification ability remaining basically unchanged,put forward a rough set of approximate attribute reduction and its methods.Finally,a concrete example demonstrates the feasibility and effectiveness of approximate attribute reduction dealing with ambiguity and uncertainty of knowledge in information systems.
Decision table
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In view of the deficiencies of attribute reduction in classic rough set, On condition that knowledge classification ability remains basically unchanged, this paper renders a new definition of the approximate attribute reduction of rough set and discuss its nature and algorithms. Theory proves that approximate attribute reduction is an extension of the traditional attribute reduction. Finally, a concrete example demonstrates the feasibility and effectiveness of approximate attribute reduction dealing with ambiguity and uncertainty of knowledge in information systems.
Variable and attribute
Decision table
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Rough set theory and its related knowledge are applied for solving actual problems of mechanical product design and the malfunction diagnosis.On the basis of analyzing and synthesizing a reduction algorithm that is based on discernibility matrix of the Rough set theory,together with the decision table,the author analyzed the application of the reduction gear malfunction diagnosis.An instance of the application showed that the method and technology are feasible.
Decision table
Basis (linear algebra)
Matrix (chemical analysis)
Table (database)
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Machine learning algorithms are known to degrade in performance when facing with many features that are not necessary in the field of artificial intelligence and pattern recognition. Rough set theory is a new effective tool in dealing with vagueness and uncertainty information. Attribute reduction is one of the most important concepts in rough set theory and application research. Once it gets the whole reduction set, the reasoning capability with multi attributes absent can behave well. There are few research papers on whole reduction set of knowledge system. Thus how to get the whole reduction set is worth a problem to research. In this paper, we first get the rough core, then calculate superset of rough reduction, finally get the whole really rough reduction set with back elimination. Experiment shows that the attribute reduction algorithm based on core and back elimination is effective.
Vagueness
Decision table
Granular Computing
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Attribute reduction is one of the key topics in the Rough Set theory field.It has been proved that computing the minimal reduction of decision table is an NP-hard problem.Relative discernibility matrix and attribute significance are considered to solve the attribute reduction of variable precision rough sets theory.Moreover,the improved algorithm of attribution reduction reduction is given.It can simplify the operation and enhance reduction the efficiency of seeking the reduction in some extent.At last,a practical example is given to show the validity of the algorithm.
Decision table
Matrix (chemical analysis)
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Attribute reduction of rough set is to remove irrelevant or unimportant attributes so as to simplify the system on the condition of the classification of information systems and the ability of decision-making remaining unchanged.An algorithm of attribute reduction based on rough set is proposed.This algorithm can make the reduction result keep the approximation by the way of calculating the importance of attributes based on the frequency of attributes appearing in the distinguish matrix.The experiment's results prove that this algorithm can simplify the teaching assessment index system,and reduce the required storage space of information systems.
Matrix (chemical analysis)
Data Reduction
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