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Decision tree classifier construction method of uncertain discrete data

A decision tree classification, discrete data technology, applied in instruments, character and pattern recognition, computer parts and other directions, can solve uncertain data classification, uncertainty and other problems, to achieve the effect of high classification accuracy

Inactive Publication Date: 2017-05-03
SICHUAN YONGLIAN INFORMATION TECH CO LTD
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Problems solved by technology

[0003] Aiming at solving the problem of uncertain data classification and improving the accuracy of its classification, a method for constructing a decision tree classifier for uncertain discrete data is proposed.

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  • Decision tree classifier construction method of uncertain discrete data
  • Decision tree classifier construction method of uncertain discrete data
  • Decision tree classifier construction method of uncertain discrete data

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Embodiment Construction

[0028] In order to solve the problem of uncertain data classification and improve the accuracy of its classification, combined with figure 1 The present invention has been described in detail, and its specific implementation steps are as follows:

[0029] Step 1. Suppose there are X samples in the training set, and the number of attributes is n, that is, n=(S 1 , S 2 ,…S n ), while splitting the attribute S i Corresponds to m classes L, where L r ∈(L 1 , L 2 ...,L m ), i ∈ (1, 2..., n), r ∈ (1, 2..., m). S i ∈(S 1 , S 2 ,…S n ), where the attribute value contains uncertainty.

[0030] Step 2: Put the uncertainty data attribute S i attribute value S ij Merge sort, according to the class pair uncertainty data attribute S i Attribute value S ij operation, denoted as the probability sum P(S ij ), the probability potential P(S ij , L r ). Its specific operation process is as follows:

[0031] Uncertain data attribute S i , its value P(S ij ) is a probability ...

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Abstract

The invention provides a decision tree classifier construction method of uncertain discrete data. The method comprises the following steps: firstly ordering by merging attribute values Sij since the attribute values of a data sample have uncertainity and discreteness, performing attribute value Sij operation on an uncertain attribute Si according to a class, and denoting as probabilistic cardinality P(Sij, Lr); and then performing splitting attribute selection according to a target function f(Si). Therefore, the constructed decision tree can realize the classification and prediction to the discrete uncertain data, and the constructed decision tree is high in classification accuracy, better avoids the problem that the information bias is large in order of magnitude, and is more suitable for an application for an actual data mining problem.

Description

technical field [0001] The invention relates to the fields of machine learning, artificial intelligence and data mining, in particular to a method for constructing a decision tree classifier for uncertain data. Background technique [0002] Decision trees are an important and active research topic in data mining and machine learning. The proposed algorithm is widely and successfully applied in practical problems such as ID 3 , CART and C4.5, decision tree these classic learning algorithms are mainly to study the problem of accuracy, and the generated decision tree has better accuracy. In recent years, the continuous progress of information technology has made uncertain data frequently appear in various research fields, such as market analysis, medical diagnosis, sensor network, mobile object tracking, environmental monitoring and other realistic scenarios, uncertain data widely exists, and It plays a vital role. However, the traditional data mining technology often ignore...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/24323
Inventor 金平艳胡成华
Owner SICHUAN YONGLIAN INFORMATION TECH CO LTD
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