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Decision rule extraction method based on rough set and attribute selection

A technology of attribute selection and extraction method, applied in special data processing applications, instruments, electronic digital data processing, etc., can solve the problems of lack of flexibility, lack of overall reduction of rule sets, insufficient reduction of rule sets, etc.

Inactive Publication Date: 2017-09-22
浙江象立医疗科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The rule extraction algorithm DRICA extracts rules from the point of view of attributes, but it can only select attributes through the classification consistency rate, which lacks flexibility, and does not perform the overall reduction of the rule set, and the reduction of the rule set is not sufficient

Method used

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  • Decision rule extraction method based on rough set and attribute selection
  • Decision rule extraction method based on rough set and attribute selection
  • Decision rule extraction method based on rough set and attribute selection

Examples

Experimental program
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experiment example

[0025] This example illustrates by taking the extraction of possible rules from the upper approximate set of decision classes as an example.

[0026] 1. Data in decision table format:

[0027] A decision table DT=(U, AT=C∪D, V, f), where U is the object set (discourse domain), C is the condition attribute set, D is the decision attribute set, V is the range, f is U and AT to V mapping.

[0028]

a1

a2

d

x1

1

1

2

x2

2

1

1

x3

1

3

1

x4

1

3

2

[0029] For the decision table above:

[0030] Object U = {x1,x2,x3,x4}

[0031] Condition attribute set C={a1,a2}

[0032] Decision attribute set D={d}

[0033] 2. Calculate the upper approximate set of each decision class:

[0034] decision class D 1 ={d=1}={x2,x3}

[0035] decision class D 2 ={d=2}={x1,x4}

[0036] Under the conditional attribute set C, D 1 upper approximation set of

[0037] Under the conditional attribute set C, D 2 upper ap...

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Abstract

The invention discloses a decision rule extraction method based on a rough set and attribute selection. The method comprises the following steps that: from an unselected attribute set, selecting a certain attribute through an existing mature attribute selection method, and then, updating a selected attribute set; utilizing the selected attribute set to extract a rule from the lower approximation of objects which are not covered with the unselected rule set; repeating the above steps until the objects in an approximation set are completely covered with the rule set; and finally, carrying out reduction on the rule set, and then, outputting the rule set. The method has the characteristics of a DRICA (Dual Regression Principal Component Analysis) algorithm and extracts the rule from the viewpoint of the attribute space; the method also has the advantages of an LEM2 (Learning from Examples Module, Version 2) algorithm, a single rule is subjected to reduction, and the rule set is integrally subjected to reduction; and in addition, an attribute selection method can be appointed, and the method is high in flexibility.

Description

technical field [0001] The invention belongs to the data mining technology in an intelligent decision system, and specifically refers to a method for extracting decision rules based on rough sets and attribute selection. Background technique [0002] In reality, the results of data collection are often accompanied by noise data, which makes uncertain mathematical tools particularly important. Compared with other theories dealing with uncertain and imprecise problems, rough set theory does not need to provide any prior knowledge other than the data set that the problem needs to deal with. Due to the superiority of rough sets in dealing with uncertain data, it has been widely used in many fields such as classification and clustering, among which the extraction of decision rules is one of the most important applications. [0003] Among the decision rule algorithms based on rough sets, the rule extraction algorithm LEM2 is more and more widely used because of its more outstandi...

Claims

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Application Information

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IPC IPC(8): G06F19/00G06F19/24
CPCG16B40/00G16Z99/00
Inventor 代建华郑国杰胡虎
Owner 浙江象立医疗科技有限公司
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