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Structure extended polynomial naive Bayes text classification method

A text classification, polynomial technology, applied in the direction of unstructured text data retrieval, text database clustering/classification, special data processing applications, etc.

Inactive Publication Date: 2015-11-11
CHINA UNIV OF GEOSCIENCES (WUHAN)
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AI Technical Summary

Problems solved by technology

However, due to the high-dimensionality of the text data itself, no structural extension method for improving the multinomial Naive Bayesian text classification model has been found so far.

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  • Structure extended polynomial naive Bayes text classification method
  • Structure extended polynomial naive Bayes text classification method
  • Structure extended polynomial naive Bayes text classification method

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Embodiment Construction

[0040] In the following, the present invention will be further described in conjunction with the embodiments.

[0041] The present invention provides a polynomial naive Bayes text classification method with extended structure, including a training phase and a classification phase, wherein,

[0042] (1) The training phase includes the following processes:

[0043] (1-1) Use the following formula to calculate the prior probability p(c) of each category in the training document set D:

[0044] p ( c ) = X j = 1 n δ ( c j , c ) + 1 n + s - - - ( 1 )

[0045] Among them, the training document set D is a known document set, and any document d in the training document set D is expressed as a word vector form d= 1 ,w 2 ,...w m > , Where w i Is the i-th word in document d, m is the number of words in training document set D; n is the number of documents in training document set D, s is the number of document categories...

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Abstract

The invention provides a structure extended polynomial naive Bayes text classification method. Firstly, a one-dependence polynomial estimator is established by using each word that occurs in a test document as a father node and then all the one-dependence polynomial estimators are subjected to weighted averaging to predict a category of the test document, wherein the weight is an information gain ratio of each word. According to the method, the structure learning phase of a Bayesian network is avoided, thereby reducing time spending brought by high dimensionality of text data; and meanwhile, the estimation process of a dual conditional probability is postponed to the classification stage, thereby ingeniously saving large space cost. According to the method, not only is classification accuracy of a polynomial naive Bayes text classifier improved, but also time spending and space cost of structure learning of the Bayesian network are avoided.

Description

Technical field [0001] The invention relates to a polynomial naive Bayes text classification method with extended structure, which belongs to the technical field of artificial intelligence data mining classification. Background technique [0002] Naive Bayes text classifier is often used to deal with text classification problems because of its simplicity and efficiency, but its attribute independence assumption makes it efficient and affects its classification performance to a certain extent. Given a document d, the document is represented as a word vector <w 1 , W 2 ,..., w m 〉, Polynomial Naive Bayes (MNB) uses formula 10 to classify document d. [0003] c ( d ) = arg m a x c A C p ( c ) Π i = 1 m p ( w i | c ) f i - - - ( 10 ) [0004] The symbol C in the above formula is a collection of class markers, m is the number of words, w i (i=1, 2,...m) is the i-th word that appears in ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/35
Inventor 蒋良孝王沙沙李超群张伦干
Owner CHINA UNIV OF GEOSCIENCES (WUHAN)
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