Method and system for classifying input data arriving one by one in time

Abstract

The invention provides a method and a system for classifying input data arriving one by one in time. The method comprises the steps of a) training a predetermined quantity of a group of classifiers by utilizing recent input data, of which the quantity increases from new to old in time and the real types are obtained, as learning samples; b) based on a recent classification result of the group of the classifiers, selecting the classifier with the highest classification precision for the recent input data from the group of the classifiers; and c) classifying the current input data by utilizing the selected classifier. According to the method and the system, concept drift does not need to be specially detected and can be automatically processed, and very high classification precision can be realized.

Description

technical field [0001] The present invention relates to a classification method and system, in particular to a method and system for classifying input data arriving one by one in time. Background technique [0002] Online learning is a machine learning method that continuously learns new data and updates existing models. It has a wide range of applications, such as streaming data mining. [0003] Concept drift is a problem unique to online learning. It refers to the conflict between data concepts before and after time, which cannot be described by a machine learning model. Constant change in the real world is at the root of concept drift. For example, in a spam classification application, emails about New Year's promotions will be treated as spam from February to October, and normal emails from November to December. [0004] see figure 1 , figure 1 A schematic diagram of a typical existing online learning method 100 is shown. In the method 100, whenever new data 110 is ...

AI Technical Summary

Problems solved by technology

These issues lead to limited classification accuracy of existing online learning methods and systems

[0007] It can be seen that due to concept drift, existing online learning methods cannot achieve data classification well.

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