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Confidence Dominance Relational Rough Set Model and Attribute Reduction Method

A technology of attribute reduction and rough set, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as incomplete order information, under-consideration of "order" system characteristics, semantic contradictions, etc.

Active Publication Date: 2017-01-18
CHONGQING INST OF GREEN & INTELLIGENT TECH CHINESE ACADEMY OF SCI
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The various extended dominance relations mentioned above have been proposed to deal with incomplete order information, but they do not consider the characteristics of the "order" system, and there are semantic contradictions under certain order characteristics.

Method used

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  • Confidence Dominance Relational Rough Set Model and Attribute Reduction Method
  • Confidence Dominance Relational Rough Set Model and Attribute Reduction Method
  • Confidence Dominance Relational Rough Set Model and Attribute Reduction Method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0070] This example first introduces the existing extended advantage relationship:

[0071] Definition 12: Extended Dominance Relationship,

[0072] use express. The restricted dominance relation is a special form of Definition 12 that allows expansion only at the attribute maximum and minimum values.

[0073] Definition 13: Finite Extended Dominance Relation,

[0074]

[0075] use express.

[0076] Definition 14: Generalized Extended Dominance Relation

[0077]

[0078] use express. The k-degree extension characteristic dominance relation is a special form of Definition 14 that subdivides missing values ​​into two cases for extension.

[0079] Definition 15: Similar Strengths Relationship

[0080] SDR + ( P ) = { ( x , y ) ∈ U 2 : ∀ q ∈ B P ...

Embodiment 2

[0095] figure 1 The flowchart of the confidence-dominant relation rough set model and the attribute reduction method provided by the embodiment of the present invention is as shown in the figure: The confidence-dominant relation rough set model and the attribute reduction method provided by the present invention include the following steps:

[0096] S1: Obtain information data and establish a decision-making system DS according to the obtained information data;

[0097] S2: Determine whether all attribute values ​​in the decision-making system have missing values, and if so, establish an incomplete and ordered decision-making system IODS;

[0098] S3: Construct the definition of confidence advantage relationship according to the incomplete ordered decision system IODS;

[0099] S4: Build a rough set model according to the definition of the confidence dominance relationship;

[0100] S5: According to the rough set model, judge whether the incomplete ordered decision-making sy...

Embodiment 3

[0152] The difference between this embodiment and Embodiment 2 is only:

[0153] The confidence advantage relation rough set model and attribute reduction method provided by the embodiments of the present invention include the following steps:

[0154] S1: Definition of confidence advantage relationship;

[0155]S2: Rough set model based on confidence dominance relation;

[0156] S3: Attribute reduction method based on identification matrix in incomplete consistent information table.

[0157] S4: In the incomplete and inconsistent information table, a heuristic attribute reduction method based on classification accuracy.

[0158] The definition of Confidential Dominate Relation (CDR), which satisfies the characteristics of order relation, satisfies reflexivity, transitivity and order symmetry; the confidence dominance relation defines that the dominance class object of an object A should be better than the known attributes of A , and the amount of known information cannot b...

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Abstract

The invention discloses a confidence dominance-based rough set analysis model and two attribute reduction methods, which are applicable to solving the incomplete preference decision problem and discovers the more important attribute to a decision under consistent or inconsistent information. The invention provides a new expanded dominance relation, namely confidence dominance relation which obeys order relation characteristics including reflexivity, transitivity and order symmetry, compared with the existing expanded dominance relation, the confidence dominance relation can avoid semantic conflict, and the approximation precision and classification accuracy of the approximate confidence dominance-based rough set analysis model are more excellent through theorem proving and instance analysis. In addition, in order to find out the more important attributes of the decision and aiming at the incompletely consistency and inconsistency conditions, the invention also discloses the two attribute reduction methods under the confidence dominance relation, including an identification matrix-based attribute reduction method and a classification precision-based heuristic attribute reduction method.

Description

technical field [0001] The invention relates to a data attribute reduction method, in particular to a confidence-dominant relation rough set model and an attribute reduction method. Background technique [0002] With the rapid development of computer network technology, the amount of data in various fields increases rapidly. However, due to the limitations of data acquisition technology, transmission failures and some human factors, data defects and loss often occur. In the real world, due to the complexity and uncertainty of the environment, people will face decision-making problems with uncertain, incomplete and decision-preferred information. [0003] Rough set theory is based on equivalence relation (satisfying reflexivity, symmetry and transitivity), and it is a new mathematical tool for dealing with uncertain and ambiguous information. Dominance-based Rough Set Analysis (DRSA) uses the dominance relation (satisfying reflexivity and transitivity) to replace the equival...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30
CPCG06F16/24564G06F16/2465
Inventor 苟光磊王国胤利节傅剑宇吴迪袁野
Owner CHONGQING INST OF GREEN & INTELLIGENT TECH CHINESE ACADEMY OF SCI
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