Double-degree integrated unbalanced data stream classification algorithm

Abstract

The invention discloses a double-degree integrated unbalanced data stream classification algorithm. The double-degree integrated unbalanced data stream classification algorithm includes a balanced data stream classification model prediction stage, a classification reliability evaluation stage and an unbalanced data stream classification model prediction stage. In the balanced data stream classification model prediction stage, firstly, a balanced data stream classification model predicts the classification of each data record. In the classification reliability evaluation stage, reliability evaluation is conducted on the classification results obtained in the balanced data stream classification model prediction stage, the classification results of the records with high reliability are directly sent back to a user, and the data records with low reliability need to be classified again in the unbalanced data stream classification model prediction stage. The method embodied in the double-degree integrated unbalanced data stream classification algorithm can be widely applied to applications such as computer-assisted clinical diagnosis and real-time intrusion detection, and the invention belongs to the field of artificial intelligence applications.

Description

technical field [0001] The invention relates to a data flow classification algorithm, in particular to a double-degree integrated unbalanced data flow classification algorithm. Background technique [0002] In recent years, data mining technology has been increasingly used in various industries, including computer-aided clinical diagnosis, Internet-based recommendation system and advertising system, customer classification, financial data analysis and abnormal transaction monitoring, etc. This industry-oriented The intelligent analysis and decision-making system has been widely accepted by people. [0003] In many practical applications, the distribution of data is unbalanced, also known as the distribution is skewed, for example, 90% of the data records belong to category A, which is called the majority category; and only 10% of the data records belong to category B, so Also known as B is the minority class. For example, in the application of financial data analysis, ...

AI Technical Summary

Problems solved by technology

Although existing classification algorithms can improve the classification accuracy of minority classes in unbalanced data streams, they reduce the classification accuracy of majority classes.

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