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Backward coarse collecting attribute reducing method using directed search

A directional search and attribute reduction technology, applied in the field of information processing, can solve problems such as one-way, poor attribute reduction results, etc., and achieve the effect of strong pertinence, strong versatility, and flexible and simple realization

Inactive Publication Date: 2005-03-02
SHANGHAI JIAO TONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The disadvantage of the heuristic is that it is unidirectional, i.e. there is only one direction in which the search is going
Computation time is greatly reduced relative to full search methods, but often produces a poor attribute reduction result

Method used

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  • Backward coarse collecting attribute reducing method using directed search
  • Backward coarse collecting attribute reducing method using directed search
  • Backward coarse collecting attribute reducing method using directed search

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Embodiment Construction

[0016] In order to better understand the technical solution of the present invention, further description will be made below in conjunction with the accompanying drawings and embodiments.

[0017] (1) Initialization:

[0018] Each attribute in the initial attribute set F according to the mutual information I(f i ; P) Rearrange from small to large, and store the sorted initial attribute set F into the directional storage area (Beam). Mutual information is arranged from small to large to facilitate finding the child equivalent attribute subsets of the first M attribute subsets in the directional storage area with the smallest redundant synergy coefficient, which can compress the directional search space and reduce the search time.

[0019] Note that the redundancy synergy coefficient describes the degree of redundancy and combination synergy of attribute subsets from the perspective of information quotient. A(A={f i | f i ∈A, i=1,..., a})F, RSC(A) is called the redundant sy...

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Abstract

The directionally searching backward coarse set attribute reducing method adopts mutual information and redundant cooperation index of the attribute subset as the measurement of the reducing coarse set attribute. The initial attribute sets are first sorted and several equivalent attribute subsets with minimum redundant cooperation index are selected from the subsets of the initial attribute sets and are stored in the directional memory area; several next level equivalent attribute subsets with minimum redundant cooperation index are then selected from the next level subsets of equivalent attribute subsets for further search; and so on until no further equivalent attribute subset can be found. The attribute subsets ultimately stored in the directional memory area are the attribute reducing result. The said method is flexible, simple and universal, and may be used in all coarse set attribute reducing fields.

Description

technical field [0001] The present invention relates to a rough set attribute reduction method, in particular to a backward rough set attribute reduction method using mutual information as a reduction measure and adopting directional (Beam) search technology, which provides a good rough set knowledge acquisition Approach belongs to the field of information processing. Background technique [0002] With the rapid development of information technology and the wide application of database management systems, people accumulate more and more data. There is a lot of important information hidden behind the surge of data, and people hope to analyze it at a higher level in order to make better use of these data. The current database system can efficiently realize data entry, query, statistics and other functions, but it cannot discover the relationship and rules existing in the data, and cannot predict the future development trend based on the existing data. The lack of means to mi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 杨胜施鹏飞
Owner SHANGHAI JIAO TONG UNIV
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