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Confidence dominance-based rough set analysis model and attribute reduction methods

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 semantic contradictions, under-consideration of "sequence" system characteristics, incomplete sequence information, etc.

Active Publication Date: 2014-03-19
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-based rough set analysis model and attribute reduction methods
  • Confidence dominance-based rough set analysis model and attribute reduction methods
  • Confidence dominance-based rough set analysis model and attribute reduction methods

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0071] This example first introduces the existing extended advantage relationship:

[0072] Definition 12: Extended dominance relation,

[0073] use express. A restricted dominance relation is a special form of Definition 12 that allows expansion only at attribute maximum and minimum values.

[0074] Definition 13: A finitely extended dominance relation,

[0075]

[0076] use express.

[0077] Definition 14: Generalized Extended Dominance Relations

[0078]

[0079] use express. The dominance relation of k-degree extended characteristics is a special form of definition 14, which subdivides missing values ​​into two cases for expansion.

[0080] Definition 15: Similar Dominance Relationship

[0081] SDR + ( P ) = { ( x ...

Embodiment 2

[0095] figure 1 The flow chart of the belief-dominant relational rough set model and the attribute reduction method provided by the embodiment of the present invention is shown in the figure: the belief-dominant relational 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 based on the obtained information data;

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

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

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

[0100]S5: According to the rough set model, judge whether the incomplete ordered decision system IODS is ...

Embodiment 3

[0153] The difference between this embodiment and embodiment 2 only lies in:

[0154] The belief advantage relational rough set model and attribute reduction method provided by the embodiments of the present invention include the following steps:

[0155] S1: Definition of the confidence advantage relationship;

[0156] S2: Rough set model based on confidence dominance relationship;

[0157] S3: An attribute reduction method based on the identification matrix in the incomplete consistent information table.

[0158] S4: A heuristic attribute reduction method based on classification accuracy in incomplete and inconsistent information tables.

[0159] The definition of Confidential Dominate Relation (CDR) satisfies the characteristics of order relations, and satisfies reflexivity, transitivity and order symmetry; Confidential Dominate Relation defines that the dominant class object of an object A should be better than the known properties of A , and the amount of known informa...

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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 belief-dominated relational 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 has increased rapidly, but due to the limitations of data collection technology, transmission failures, and some human factors, data defects and losses often occur. In the real world, due to the complexity and uncertainty of the environment, people will face decision-making problems with uncertain and incomplete information and decision-making preference information. [0003] Rough set theory is based on equivalence relations (satisfying reflexivity, symmetry and transitivity), and is a new mathematical tool for dealing with uncertain and ambiguous information. Dominance-based Rough Set Analysis (DRSA) uses dominance relations (satisfying reflexivity and transitivity) to r...

Claims

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Application Information

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Patent Type & Authority Applications(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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