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Data discretization method based on category-attribute relation dependency

A discretized and dependent technology, applied in electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as information loss, unreasonableness, and impact on machine learning accuracy, and achieve the effect of reducing inconsistency and high precision.

Inactive Publication Date: 2010-05-05
DALIAN UNIV OF TECH
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AI Technical Summary

Problems solved by technology

First of all, the importance of attributes is not considered in the process of discretization; secondly, the consideration of inconsistency rate is lacking; finally, it is unreasonable to use caim value as discriminant
This often results in loss of information, which affects the accuracy of machine learning

Method used

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  • Data discretization method based on category-attribute relation dependency
  • Data discretization method based on category-attribute relation dependency
  • Data discretization method based on category-attribute relation dependency

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Experimental program
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Embodiment Construction

[0048] The specific process of the Improved CAIM program is as follows:

[0049] Input: A dataset with m instances, t decision classes and s conditional attributes.

[0050] The first stage:

[0051] (1) Calculate the difference set of each attribute, and sort the attributes in order of importance from small to large a 1 , a 2 ,...a s (a 1 Represents the least important attribute, a s represents the most important attribute)

[0052] (2)For(a i =a 1 ;i<=s;i++)

[0053] {

[0054] Step1:

[0055] find attribute a i The minimum value in x min and the maximum value x max ;

[0056] attribute a i All the different values ​​​​in are arranged in ascending order {x min , x 2 ,...x max};

[0057] Calculate the intermediate value between all adjacent different values ​​as a candidate breakpoint, the calculation formula is

[0058] d i = x i + ...

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Abstract

The invention discloses a data discretization method based on category-attribute relation dependency, belonging to the field of data mining. The method is characterized by comprising: first, based on CAIM algorithm, comprehensively considering about the influence of attribute importance and inconsistent rate of a decision table for discretization result, and providing an improved CAIM algorithm; and second, adopting a lambda correlation coefficient as discretization discriminant for evaluating category-attribute relation, and providing a new monitor discretization algorithm which does not need artificial input parameters and can automatically select discretization points. The method has the advantages that the method can maintain the high efficiency of extracting information for an original data set, balances the consideration of accuracy, and can obtain higher accuracy when in machine learning.

Description

technical field [0001] The invention belongs to the field of data mining and relates to a continuous attribute discretization algorithm in machine learning, in particular to a CAIM (Class-Attribute Interdependence Maximization) algorithm based on class-attribute relationship dependency. Background technique [0002] In the past, in the field of data mining, the research on discretization algorithms was usually considered as an auxiliary work and did not receive due attention. It was not until recent years with the rapid development of knowledge discovery and machine learning that it attracted the attention of researchers. focus on. Data sets from real life often involve continuous numerical attributes. However, many current machine learning algorithms can only deal with data sets that only contain discrete-valued attributes, which brings inconvenience to the research of machine learning. Typical machine learning algorithms such as decision trees and association rules can on...

Claims

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Application Information

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IPC IPC(8): G06F17/30
Inventor 李克秋王哲桑雨申严明
Owner DALIAN UNIV OF TECH
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