Text categorization method based on Xgboost categorization algorithm

Abstract

Provided is a text categorization method based on an Xgboost categorization algorithm. According to the text categorization method, a characteristic value is calculated by extracting a tagged word through Labeled-LDA, and then text categorization is conducted by using the Xgboost categorization algorithm. Compared with a method that the text categorization is conducted by using a common categorization algorithm and a common vector space modal is adopted as characteristic space, the method reduces required consumed memory, this is because the number of words contained in a Chinese text is several million, dimensionality is high, if the words are adopted as characteristics, the consumed memory is massive, even one machine cannot conduct processing, however, the number of common Chinese characters is no more than ten thousand, the number of frequent Chinese characters is even two to three thousand, the dimensionality is reduced greatly, and meanwhile Xgboost supports input in a dictionary mode rather than an array mode. Besides, the invention provides a novel feature selection algorithm Labeled-LDA algorithm with latent semantic and supervision, the Labeled-LDA is adopted to conduct feature selection, and thus not only can semantic information of massive linguistic data be dug by utilizing LDA, but also class information contained in the text can be utilized. Furthermore, preprocessing is easy, there is no need to extract the characteristics carefully, and accuracy and performance of categorization are improved with the addition of the strong ensemble learning algorithm Xgboost supporting a distributed mode.

Description

technical field [0001] The present invention relates to the field of text classification, more specifically, to a text classification method based on Xgboost classification algorithm. Background technique [0002] Text classification methods have been widely used in search engines, personalized recommendation systems, public opinion monitoring and other fields, and are an important part of efficient management and accurate positioning of massive information. [0003] The common framework of text classification methods is based on machine learning classification algorithms, which include data preprocessing, followed by feature extraction, feature selection, feature classification and other steps. [0004] Feature extraction is to use a unified method and model to identify the text. This method or model can represent the characteristics of the text and can be easily converted into a mathematical language, and then converted into a mathematical model that can be processed by a ...

AI Technical Summary

Problems solved by technology

[0015] The present invention provides a text classification method based on the Xgboost classification algorithm to solve the defects of low classification performance, large memory consumption and low classification accuracy of the methods provided by the above prior art

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