Continuous attribute discretization method based on Chi2 statistics

A discretization and attribute technology, applied in complex mathematical operations and other directions, can solve problems such as inconsistency, achieve high precision, and avoid unfair interval selection.

Inactive Publication Date: 2010-07-14
DALIAN UNIV OF TECH
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Problems solved by technology

[0016] 3) I...

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  • Continuous attribute discretization method based on Chi2 statistics
  • Continuous attribute discretization method based on Chi2 statistics
  • Continuous attribute discretization method based on Chi2 statistics

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Embodiment Construction

[0037] 1. The specific process of Rec-Chi2 algorithm is as follows:

[0038] Step1: Initialization. Let the significance level α=0.5. Calculate the information system inconsistency rate Incon_rate.

[0039] Step2: Sort the data for each attribute and calculate the χ of all adjacent intervals according to formula (1) 2 Value, find out the χ 2 corresponding Value, then calculate the difference

[0040] Step3: Merge

[0041] while (intervals that can be combined)

[0042] {Find the largest breakpoint of D′ to merge;

[0043] if(Incon_rate increases)

[0044] {Undo the merge; goto Step4;}

[0045] else goto Step2;

[0046] }

[0047] Step4: if (α is the last level)

[0048] {Exit the program, discretization is complete;}

[0049] else{α 0 =α; downgrade to α; goto Step2;}

[0050] Step5: Discretization of a single attribute.

[0051] For each attribute

[0052] {Calculate the difference D′;

[0053] α=α 0 ;

[0054] Flag = 0;

[0055] ...

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Abstract

The invention relates to a continuous attribute discretization method based on Chi2 statistics, which belongs to the field of data mining, and is characterized in that Chi2 series algorithm is analyzed, a discretization standard is re-defined, and continuous attributes are discretized more reasonably and more effectively; and a difference sequence interval combination method serves as a standard, so that all adjacent areas have fair combination opportunities, thereby well solving the problem of unfairness. In addition, the imprecise Eji value in Chi2 statistics is analyzed, and two improving schemes are presented. The continuous attribute discretization method based on Chi2 statistics has the advantages and the benefits that the high efficiency in extracting information from original data, the discretization process is more precise, and higher accuracy is realized in machine learning.

Description

Technical field [0001] The invention belongs to the field of data mining and relates to a χ 2 The continuous attribute discretization method of statistics involves the Chi2 series algorithm based on probability and statistics. Background technique [0002] Data in real life often contains continuous-valued attributes. However, current induction and classification algorithms often rely on discrete-valued attributes, which brings inconvenience to the research of machine learning. Therefore, continuous attribute discretization plays a very important role in data mining, machine learning, and knowledge discovery, and has attracted the attention of researchers. The task of discretization is to divide the value range or interval of the continuous attribute into a few small cells, where each cell corresponds to a discrete symbol. With the widespread attention and in-depth research in this field, the discretization algorithm has been greatly developed. The current discretization types a...

Claims

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Application Information

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IPC IPC(8): G06F17/10G06F17/18
Inventor 李克秋桑雨王哲
Owner DALIAN UNIV OF TECH
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