A Text Classification Algorithm Based on Mixed Multinomial Distribution

Abstract

The present invention provides a text classification algorithm based on mixed multinomial distribution, including the following steps: S1: input training set text; S2: calculate and save the probability distribution of all text categories C; S3: initialize the parameter value θ of mixed multinomial distribution , π k And the number of components K; S4: Use the current parameter values ​​θ, π k , calculate the log-likelihood function of the complete data on the expectation of the posterior probability distribution of the hidden variable; S5: use the EM algorithm to train the parameter values ​​θ and π of the mixed multinomial distribution k ; S6: For the different numbers of components K, draw the prediction error graphs of the model against the test set and the training set, and select the K value with the smallest prediction error; S7: Output the result. The beneficial effect of the present invention is that the present invention combines naive Bayesian algorithm with mixed multinomial distribution, uses EM algorithm to estimate the parameters of the mixed model, so as to improve the classification accuracy of the model.

Description

technical field [0001] The invention relates to a text classification algorithm, in particular to a text classification algorithm based on mixed multinomial distribution. Background technique [0002] With the emergence of a large number of online texts and the rise of machine learning, large-scale text classification and retrieval have aroused the interest of researchers. A large number of results show that the method based on statistical learning has high text classification accuracy and can be applied to any field of learning, making it the mainstream method of text classification. [0003] The traditional text classification algorithm based on Naive Bayes assumes that under a given text category, each text feature vector attribute is independent and identically distributed. Due to the simple assumption, the text classification algorithm based on Naive Bayes has a small computational complexity. In some cases, better classification results can also be achieved. However, ...

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Problems solved by technology

[0003] The traditional text classification algorithm based on Naive Bayes assumes that under a given text category, each text feature vector attribute is independent and identically distributed. Due to the simple assumption, the text classification algorithm based on Naive Bayes has a small computational complexity. In some cases, better classification results can also be achieved. However, in actual tasks, this conditional independence assumption is difficult to establish and has great limitations. It is necessary to consider relaxing the attribute conditional independence assumption to a certain extent.

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