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Improved uncertain continuous attribute decision tree constructing method

A construction method and uncertainty technology, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve problems such as classification and prediction of uncertain continuous attributes, decision trees of uncertain continuous attributes, etc., to avoid transition fitting problem, 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

[0004] Aiming at solving the problem of classification and prediction of uncertain continuous attributes and improving the accuracy of classification and prediction, the problem of class determination and the problem of overfitting in decision trees, an improved uncertain continuous attribute decision tree is proposed. build method

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  • Improved uncertain continuous attribute decision tree constructing method
  • Improved uncertain continuous attribute decision tree constructing method
  • Improved uncertain continuous attribute decision tree constructing method

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Embodiment Construction

[0028] In order to solve the problem of classification and prediction of uncertain continuous attributes and improve the accuracy of its classification and prediction, the problem of class determination and the problem of transition fitting in decision trees, 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 of uncertain continuous attributes, 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 is uncertain.

[0030] Step 2: Put the uncertain continuous data attribute S i attribute value S ij Merge sort, according to class pair uncertain continuous data attribute S i Attribute value S ij operation, denoted as the probability su...

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Abstract

The invention provides a decision tree classifier construction method based on uncertain continuous attribute. X sample data are classified. Uncertain continuous data are in the sample data. The attribute values Sij of uncertain continuous data attributes Si are merged and sorted. According to the class, attribute value Sij operation is carried out on the uncertain data attributes Si, and probability and P(Sij) are recorded. The class is processed to acquire the probability potential P(Sij, Lr) of each branch attribute value, and a decision tree is created. Split Si is selected according to the objective function created according to the invention. According to conditions, tree constructing is stopped. The constructed decision tree is able to avoid the problem that information bias is large order of magnitudes. Classification and prediction functions of an object with uncertain continuous attributes can be realized. Compared with a decision tree in the prior art, the constructed decision tree has higher classification accuracy; the constructed decision tree is more suitable for the application of actual data mining; the constructed decision tree can better determine the subordinate class under multiple branches of uncertain attributes; and the constructed decision tree better avoids transition fitting.

Description

technical field [0001] The invention relates to the fields of machine learning, artificial intelligence and data mining, in particular to an improved construction method of an uncertain continuous attribute decision tree. Background technique [0002] Decision tree research is an important and active research topic in data mining and machine learning. The proposed algorithm is widely used in practical problems, such as ID 3 , CART and C4.5, this kind of algorithm is mainly to study the problem of accuracy. With the advancement of science and technology, in recent years, uncertain data frequently appear in practical applications, including wireless sensor networks, radio frequency identification, privacy protection and other fields. Its data feature is that the data value is not definite, that is, it represents a data point, and its representation method is to take multiple values ​​as a whole, and use a certain probability distribution as the corresponding possibility of e...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/24323G06F18/214
Inventor 金平艳胡成华
Owner SICHUAN YONGLIAN INFORMATION TECH CO LTD
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