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Method for extracting classification rule based on fuzzy-rough model

A fuzzy and rough, extraction method technology, applied in special data processing applications, instruments, electrical digital data processing and other directions, can solve the problems of continuous attribute value fuzzy boundary not taking into account, complex data mining process, loss of important data information, etc.

Inactive Publication Date: 2011-06-15
XIAN UNIV OF POSTS & TELECOMM
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[0011] Therefore, in summary, the shortcoming of the current continuous attribute discretization method is that the fuzzy boundary of the continuous attribute value is not considered, so in the discretization process, if there are too many discrete intervals, the subsequent data mining process will be too complicated The mining rules are not refined and accurate; if there are too few discrete intervals, important data information will be lost

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  • Method for extracting classification rule based on fuzzy-rough model
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  • Method for extracting classification rule based on fuzzy-rough model

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[0044] A method for extracting classification rules based on a fuzzy rough model, comprising:

[0045] 1. Fuzzification of continuous attributes

[0046] (1) Decision-making system for continuous attribute values

[0047] Suppose a decision system (U, Q, V, f), where U={x 1 , x 2 ,Λ,x n} is a non-empty finite universe, representing an object; Q is a non-empty attribute set, Q=CY{d}, C={q 1 ,q 2 ,Λ,q m} is a non-empty, finite conditional attribute set, {d} is a decision attribute set, d: U→{1, 2, Λ, g}; V is an attribute value, V=V c YV d , V C ={V q : q∈C} is the set of conditional attribute values, V d is the decision attribute value set, and the attribute value v of the i-th object under the j-th conditional attribute ij (1=1Λn, j=1Λm) is a continuous attribute value; f: U×Q→V is an information mapping function, obviously this is a decision system with continuous attribute value.

[0048] (2) Attribute fuzzification

[0049] In practical applications, the key to...

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Abstract

The invention relates to a method for extracting a classification rule based on a fuzzy-rough model. Since fuzzy boundaries of continuous attribute values are not considered in the conventional continuous attribute discretization method, a data mining rule is not refined or accurate enough and important data information is easy to lose during discretization. The method for extracting the classification rule comprises the following steps of: performing attribute fuzzification on continuous attributes in an information sheet by using a membership function in a fuzzy set; and extracting parameters such as approaching precision approximate measure, rough approaching precision approximate measure, approaching precision classification quality measure, approaching precision relative classification measure and the like by using a rough set in a fuzzy similarity relation so as to establish an approaching approximate-based fuzzy-rough set reduction algorithm to solve the classification rule. In the method, each continuous attribute is added into an attribute reduction set in a descending order according to importance until the reduction condition is met, and particularly, the attribute reduction can be quickly solved when multiple condition attributes are available.

Description

Technical field: [0001] The invention belongs to the data mining technology in an intelligent decision support system, and relates to a model classification rule extraction method, in particular to a classification rule extraction method based on a fuzzy rough model. Background technique: [0002] Rough set theory is a mathematical tool for analyzing data. Its characteristic is that it does not need to pre-specify the quantitative description of certain characteristics or attributes, but directly starts from the description set of a given problem to find out the internal laws of the problem. It has the advantages that knowledge extraction is completely driven by data without artificial assumptions, the expression space of input information is simplified, and the algorithm is simple and easy to operate. However, the mathematical basis of rough sets is set theory, and its ability to deal with continuous attributes in information tables is very limited. At present, for the dat...

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Application Information

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IPC IPC(8): G06F17/30
Inventor 张文宇
Owner XIAN UNIV OF POSTS & TELECOMM
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