Multi-label data flow classification method based on incremental learning

Abstract

The invention discloses a multi-label data stream classification method based on incremental learning, and the method comprises the steps: step 1, an initial training stage: carrying out the modelingof a multi-label data stream into data blocks with a fixed instance number, carrying out the naive Bayes model training of each data block according to the initial data block, and obtaining a clustercenter set through employing a KMeans algorithm, wherein the trained naive Bayes classification model and the cluster center set jointly serve as a base classifier; step 2, a concept drift detection stage: when the number of the base classifiers in the naive Bayes integration model reaches a certain number in the initial learning stage, carrying out concept drift detection from the data level andthe model level respectively; step 3, an increment updating stage: when a latest data block Dt comes, updating the base classifier by using information carried by each sample in the Dt for each base classifier in the integration model, and performing instance information updating. The concept drift in the data flow can be detected in time, the situation that the algorithm performance encounters large downslide when the concept drift occurs is avoided, the latest data can be subjected to incremental learning, and the performance of the model is guaranteed.

Description

technical field [0001] The invention relates to a multi-label classification method in a data flow environment, in particular to an incremental learning and concept drift detection method. Background technique [0002] In recent years, with the rapid development of the Internet, more and more data are generated every day. We are currently in an era of rapid data growth, and the global Internet generates massive amounts of data every moment. Especially in the field of social networking, the number of daily active users of Tencent's WeChat has reached 900 million, and the number of active users is generating high-speed, dynamic massive data all the time. Similar scenarios include sensor networks, website logs, e-commerce transaction records, and so on. This kind of data generated in chronological order is called data stream, and mining and analysis of these data streams is a realistic and challenging problem. In the field of single-label data stream classification, academia...

AI Technical Summary

Problems solved by technology

However, in some existing methods, concept drift is dealt with in an adaptive manner, and the problem of concept drift cannot be detected and dealt with in time.

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