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Interval-valued fuzzy rough set attribute selection method based on Gini indexes

A technology of fuzzy rough set and Gini index, applied in the direction of complex mathematical operations, etc., to achieve the effect of removing redundant attributes, simple calculation, and reducing noise interference

Inactive Publication Date: 2017-12-05
TIANJIN UNIV
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  • Summary
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  • Application Information

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Problems solved by technology

However, there are many interval-valued data in real life, and there are few existing methods for interval-valued data.

Method used

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  • Interval-valued fuzzy rough set attribute selection method based on Gini indexes
  • Interval-valued fuzzy rough set attribute selection method based on Gini indexes
  • Interval-valued fuzzy rough set attribute selection method based on Gini indexes

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experiment example 1

[0086] By running the method of the present invention on the actual data set fish, its effectiveness is shown. The results of the operation are shown in Table 1 and Table 2: irrelevant and redundant attributes are eliminated, thereby improving data quality and improving the generalization ability of the classifier. Among them, the data set comes from the public UCI data warehouse (http: / / archive.ics.uci.edu / ml); the data set after attribute selection is the original data set to remove the attributes not in the attribute selection; the classification accuracy is The average value of ten cross-validation, the classifier used is KNN (k=5), J48, Random Forest.

[0087] Table 1 The number of attributes after attribute selection and the number of original attributes

[0088]

[0089] Table 2 The correct rate of attribute classification

[0090] threshold

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Abstract

The present invention provides an interval value fuzzy rough set attribute selection method based on Gini index, comprising the following steps: Step 1, selecting an interval value decision system IVDS=(U, C∪D), where U is the universe of discourse and C is a conditional attribute Set, D is the decision attribute set, giving the similarity rate α and the stopping condition ε; step 2, use the RBD similarity to construct the similarity matrix of each object in the universe U in step 1; use the similarity rate α to obtain the universe U The α-similar class of each object ui relative to other objects uj in and other steps; this method introduces the Gini index into the rough set, defines the attribute importance formula, and proposes an attribute selection algorithm for interval values.

Description

technical field [0001] The invention relates to an attribute selection method, in particular to an interval value fuzzy rough set attribute selection method based on the Gini index. 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 set in dealing with uncertain data, it has been widely used in many fields such as classification and clustering, among which attribute selection is one of the most important applications. Attribute selection can eliminate redundant, irrelevant attributes from a large number of attributes, thereby improving data quality, speeding up data processing, and improving the generalization ability of clas...

Claims

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Application Information

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IPC IPC(8): G06F17/18
CPCG06F17/18
Inventor 代建华郑国杰
Owner TIANJIN UNIV
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