Method for selecting knowledge of computer-assisted decision making system based on undistinguishable relation

A computer-aided knowledge selection technology, applied in the evaluation field of knowledge matching discrimination and screening, can solve the problems that affect the flexible and efficient application of knowledge, and are not enough to describe the supporting strength and degree of auxiliary decision-making

Active Publication Date: 2014-06-18
THE 28TH RES INST OF CHINA ELECTRONICS TECH GROUP CORP
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the above-mentioned theoretical methods are not enough to describe the support strength and degree of different knowledge granularities for decision-making assistance, thus affecting the flexible and efficient application of knowledge in decision-making assistance tasks in computer intelligent decision-making assistance systems

Method used

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  • Method for selecting knowledge of computer-assisted decision making system based on undistinguishable relation
  • Method for selecting knowledge of computer-assisted decision making system based on undistinguishable relation
  • Method for selecting knowledge of computer-assisted decision making system based on undistinguishable relation

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0083] Suppose the target database is a triplet S=(U,C,d), where U represents the target set, consisting of 10 targets x 1 ,x 2 ,...,x 10 Composition, ie U={x 1 ,x 2 ,...,x 10}, C={c 1 ,c 2 ,c 3 ,c 4} represents the target attribute set, consisting of 4 attributes, d represents the target category, and each record in the target database corresponds to a target x i (1≤i≤10), including target x i The values ​​of the 4 attributes f(x,c 1 ), f(x,c 2 ), ... and f(x,c 4 ), and its target class f(x i , d). At the same time, it is assumed that there is only one piece of knowledge K in the existing knowledge set Ω={K} in the knowledge collection database, that is, |Ω|=n=1, and the knowledge K={c 1 ,c 2}; and assume that the threshold threshold is α=0.9. The target database is shown in the table below. Calculate the decision attribute support vector of knowledge K according to steps 4.1-4.13.

[0084] Table 1 Target database

[0085]

[0086] Step 4.1, constructing...

Embodiment 2

[0115] Assume that on the basis of the target database (Table 1) in the implementation case 1, there is a knowledge set Ω={K 1 , K 2 , K 3}, where knowledge K 1 ={c 1 ,c 2}, knowledge K 2 、K 3 is newly added, and K 2 ={c 3},K 3 ={c 4}, firstly, according to the indistinguishable relationship sets and decision attribute support vectors of the three pieces of knowledge generated in steps 4.1-4.13, assuming that the threshold threshold is α=0.7, then output the final optimized knowledge set according to steps 4.14-4.15.

[0116] For Article 1 Knowledge K 1 ={c 1 ,c 2} The set of indistinguishable relations constructed is: U / IND(K 1 )={{x 1 ,x 2 ,x 3},{x 4 ,x 5 ,x 6},{x 7 ,x 8},{x 9 ,x 10}}. For Article 2 Knowledge K 2 ={c 3} The set of indistinguishable relations constructed is: U / IND(K 2 )={{x 1 ,x 2 ,x 3 ,x 4},{x 5 ,x 6 ,x 7},{x 8 ,x 9 ,x 10}}; for article 3 knowledge K 3 ={c 4} The set of indistinguishable relations constructed is: U / IND(...

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Abstract

The invention discloses a method for selecting knowledge of a computer-assisted decision making system based on an undistinguishable relation. The method is characterized in that a computer comprises a central processing unit and a memorizer, the computer is connected with data collecting equipment, data collected by the data collecting equipment are a set of attribute values and target categories for monitoring targets, and the attribute values are in a continuous type or a discrete type; a target database, a knowledge set base, a basic knowledge base, a session module and a question processing module are stored in the memorizer; the central processing unit controls the memorizer and executes the first step of constructing the target database, the second step of determining decision making questions, the third step of constructing the knowledge set base, the fourth step of selecting the knowledge and the fifth step of updating the basic knowledge base.

Description

technical field [0001] The invention relates to the field of information processing, analysis and management of computer-aided decision-making, in particular to an evaluation method for knowledge matching, discrimination and screening of a computer-aided decision-making system. Background technique [0002] In the field of computer intelligence-aided decision-making, a variety of knowledge-granularity computing models have been proposed in the academic community. The most important of these are fuzzy sets, rough sets and quotient space theory. The fuzzy set model is proposed by Zadeh according to the fuzzy set theory, and uses the method of fuzzy mathematics to study the methods and theories related to granularity calculation. The rough set model was proposed by Pawlak in the early 1980s. The theory of quotient space was proposed by Zhang Ba and Zhang Ling in my country. Under the quotient space model theory, concepts are represented by subsets, and concepts of different ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N5/00
Inventor 徐欣易侃周方张金锋
Owner THE 28TH RES INST OF CHINA ELECTRONICS TECH GROUP CORP
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