A Network Video Classification Method Based on Historical Access Records

Abstract

The invention relates to a network video classification method based on historical access records and belongs to the technical field of computer network data mining. The method comprises, firstly, automatically analyzing historical access record datasets of videos, extracting meaningful characteristics, generating standby data files for the historical access record datasets of the videos, converting historical access records into structurized documents applicable to training through the data files and then performing machine learning on the structurized documents through logistic regression to obtain prediction models; utilizing the prediction models, according to the integrity of the historical access record information of videos to be predicted, to select corresponding methods to perform classification prediction on the videos to be predicted. Compared with the prior art, the network video classification method based on the historical access records can reduce labor costs and simplify parameters involved in computation and is more accurate in prediction effects and lower in time consumption. Meanwhile, due to the fact of being capable of being clustered or not according to the integrity of the historical access record information of the videos to be predicted, the models has a wide application range.

Description

technical field [0001] The invention relates to a network video classification method, which belongs to the technical field of computer network data mining. Background technique [0002] With the rapid development of database technology, the wide application of database management systems and the rapid popularization of the Internet, the amount of historical access records of video (hereinafter referred to as video) on the Internet has increased dramatically. Behind the surge of data lies a large number of "treasures", that is, previously unknown and potentially useful information. In the face of large-scale massive data, data mining technology emerges as the times require, and extracts hidden but unknown but hidden information from a large number of incomplete, noisy, fuzzy, random, and practically applied data. And useful information and knowledge of the process. [0003] The tasks of data mining mainly include classification, prediction, association analysis, time serie...

AI Technical Summary

Problems solved by technology

However, the existing predictive methods based on logistic regression have some limitations in the streamlining of data sets, such as only sorting the importance of attributes while ignoring the discrete distribution of values, not considering the correlation between attributes, etc.

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