Method and system for multi-label active learning classification

Abstract

The invention provides a method and a system for multi-label active learning classification. The method comprises: respectively using a logarithm likehood method to obtain likehood ratio of a labeled sample label pair and using an entropy method to obtain nondeterminacy of a to-be-labeled sample label pair; respectively calculating KL distances of different labels of a plurality of same samples, and weight factors of a plurality of different labels; performing multiplication on each KL distance and the corresponding weight factor, to obtain a corresponding result, adding the plurality of results, to obtain the KL distance sum of the to-be-labeled sample label pair related to the to-be-labeled sample label pair, using the KL distance sum to determine the nondeterminacy of cross labels; determining final nondeterminacy of the to-be-labeled sample label pair; and using the likehood ratio and the final nondeterminacy of the to-be-labeled sample label pair to obtain a new sample label pair training set, and using the new sample label pair training set to train a classifier.

Description

technical field [0001] The present invention relates to the technical field of machine learning, and more specifically, relates to a multi-label active learning classification method and system. Background technique [0002] With the development of information technology, the importance of multi-label data classification technology is gradually highlighted, so that the application of corresponding multi-label data classification technology is also increasing, for example, semantic annotation of images and videos, biological gene function classification, text classification, etc. . As a modeling tool for ambiguous objects, multi-label learning is a learning method that is more in line with the laws of the real and objective world. Under this framework, each object no longer corresponds to a unique label. The purpose of multi-label learning is to provide Unseen objects are assigned the appropriate label set. Due to the complexity of multi-label classification problems, it ta...

AI Technical Summary

Problems solved by technology

[0003] The existing multi-label data classification technology only considers the uncertainty of the label of a single sample to be tested in the process of obtaining labeled data, but does not involve the uncertainty between the labels of the samples to be tested, so that after the labeling When the label of the sample to be marked is marked, the accuracy of the label is affected, and the accuracy of the classifier is affected.

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