A Structure-Extended Multinomial Naive Bayesian Text Classification Method

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 structure-expanded polynomial naive Bayesian text classification method, which belongs to the technical field of artificial intelligence data mining classification. Background technique [0002] Naive Bayesian text classifier is often used to deal with text classification problems because of its simplicity and efficiency, but its attribute independence assumption affects its classification performance to some extent while making it efficient. Given a document d, the document is represented as a word vector in the form <w 1 ,w 2 ,...,w m >, Multinomial Naive Bayes (MNB) uses Equation 10 to classify document d. [0003] [0004] In the above formula, the symbol C is the set of class labels, m is the number of words, and w i (i=1,2,...m) is the i-th word appearing in document d, f i is the word w i The frequency of appearance in document d, the prior probability p(c) is estimated by formula 1, and the conditional ...

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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