Sequence information parallel reduction method based on attribute separation
An attribute and attribute set technology, applied in informatics, special data processing applications, instruments, etc., can solve the problems affecting the reliability and effectiveness of reduction, lack of calculation methods, and time-consuming to solve reduction, etc. Space overhead, approximation accuracy and classification accuracy are excellent, and the effect of avoiding contradictions
Inactive Publication Date: 2018-02-23
LUSHAN COLLEGE OF GUANGXI UNIV OF SCI & TECH
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Problems solved by technology
In rough set theory, the attribute reduction of sequence information system is an important problem. At present, some preliminary research results have been obtained, but there is still a lack of effective calculation methods. The main performance is that it takes too long to solve the reduction, which seriously affects the Reliability and Validity of Reduction
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[0017] In order to make the purpose, technical solutions and advantages of the present invention clearer, the preferred embodiments are listed below, and the present invention is further described in detail. However, it should be noted that many of the details listed in the specification are only for readers to have a thorough understanding of one or more aspects of the present invention, and these aspects of the present invention can be implemented even without these specific details.
[0018] A method for parallel reduction of sequence information based on attribute division according to the present invention comprises the following steps:
[0019] Step 1: Establish a decision system S for the input initialization sample set S=(U,A,V,f) Ai =(U,A i ,V Ai , f Ai ), and according to the decision system S Ai Perform density division to form a reduced attribute set; where U={x 1 ,x 2 ,...,x n} is a non-empty collection of objects, A={a 1 ,a 2 ,...,a m} is an attribute s...
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The invention relates to a sequence information parallel reduction method based on attribute separation. The method comprises the following steps: I, establishing decision systems of input initialization sample sets, and performing density separation according to the decision systems so as to form reduction generation attribute sets; II, establishing preponderance residual quantities of differentattribute sets for the reduction attribute sets formed by the initialization samples and the decision systems, and acquiring super reduction of a maximum decision system; III, inspecting attributes ofthe super reduction one by one, sequencing the attributes of the super reduction, and gradually inserting the preponderance residual quantities into the reduction attributes; IV, performing redundancy inspection on the attributes of the super reduction, and outputting a final reduction optimal solution. By adopting the method, decision systems are separated according to attribute density, furthermore super reduction and reduction of the decision systems are dissolved on the basis, the reduction dissolution time is shortened, and the reduction reliability and the reduction effectiveness are improved.
Description
technical field [0001] The invention relates to the field of rough set attribute reduction, in particular to a parallel reduction method of order information based on attribute division. Background technique [0002] As a data analysis and processing theory, rough set theory was proposed by Polish scientist Z. Pawlak in 1982. It is another theoretical tool for dealing with uncertainty after probability theory, fuzzy set and evidence theory. It can effectively analyze and reason data, discover hidden knowledge from imprecise, inconsistent and incomplete information, and reveal potential laws. Rough set has been paid more and more attention in recent years, and it is one of the current research hotspots in the field of artificial intelligence theory and its application in the world. Its effectiveness has been confirmed in many successful applications in the fields of science and engineering. The unique advantages of certain, imprecise and incomplete information have attracted...
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IPC IPC(8): G06F19/00
CPCG16Z99/00
Inventor 莫京兰
Owner LUSHAN COLLEGE OF GUANGXI UNIV OF SCI & TECH