Multi-strategy combined document automatic classification method

Abstract

The invention discloses a multi-strategy combined document automatic classification method which comprises the steps: extracting keywords from a given to-be-classified document, obtaining a vector of the to-be-classified document according to the keywords, and obtaining a class center vector of each class in a standard document base; obtaining a class threshold value of a classified document according to the class centre vector; carrying out comparison of the similarity degree of the vector of the to-be-classified document and the class centre vector with the class threshold value, when the similarity degree of the vector of the to-be-classified document and the class centre vector is larger than the class threshold value, classifying the to-be-classified document through adoption of a space vector model; and if the similarity degree of the vector of the to-be-classified document and the class centre vector is not larger than the class threshold value, classifying the to-be-classified document through adoption of a k-nearest neighbor (KNN) method. Compared with the prior art, through the technical scheme, the multi-strategy combined document automatic classification method can greatly improve efficiency of the document classification and cannot reduce classification precision at the same time.

Description

technical field [0001] The invention relates to a computer document classification method, in particular to a multi-strategy combined document automatic classification method. Background technique [0002] Automatic Text Categorization (Automatic Text Categorization), or text classification for short, refers to the process of using a computer to attribute an article to a predetermined category or categories. Accurate and efficient classification of text is an important part of many data management tasks and an important part of text mining. Before the 1990s, the dominant text classification method has always been the classification method based on knowledge engineering, that is, manual classification by professionals. Manual classification is very time-consuming and inefficient. Since the 1990s, many statistical methods and machine learning methods have been applied to automatic text classification, and the research on text classification technology has aroused great inter...

AI Technical Summary

Problems solved by technology

Another disadvantage of the KNN method is that the amount of calculation is large, because for each text to be classified, the distance to all known samples must be calculated to obtain its K nearest neighbors

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