Active learning sample selection strategy integrated with confidence criterion and diversity criterion

Abstract

The invention relates to an active learning sample selection strategy integrated with a confidence criterion and a diversity criterion. The active learning sample selection strategy comprises the following steps: training a model Mt based on an existing labeled data set DL; predicting a current unlabelled data set DU by using the Mt to obtain a predicted vector set Pt; calculating an information entropy of each sample according to the Pt, and selecting front K samples each having a largest entropy; extracting feature representations of K unlabelled samples according to the Mt to obtain a feature vector set Ft; performing density peaks clustering on the Ft, respectively selecting corresponding proportion and number of samples from a center of a cluster generated by the density peaks clustering, and an edge point and an outlier of the cluster, handing the samples to an expert for labeling, adding the labeled data set DL, and simultaneously deleting corresponding samples from the unlabelled data set DU; updating the Mt by using the current labeled data set DL to obtain Mt + 1; and repeating the above steps till labeling of all samples is ended or reaches to a designated number of iteration times to complete a whole algorithm flow.

Description

technical field [0001] The invention relates to an active learning sample selection strategy, in particular to an active learning sample selection strategy that combines confidence criteria and diversity criteria, and belongs to the technical field of computer applications. Background technique [0002] Statistical learning techniques have been widely used in recent years. When using some traditional supervised learning methods for classification, the larger the training data, the better the classification effect. However, in many scenarios in real life, it is difficult to obtain labeled samples, which requires experts in the field to manually label, which requires a lot of time and economic costs. Moreover, if the size of the training samples is too large, the time spent on training will be relatively large. So is there a way to use fewer training samples to obtain a classifier with better performance? Active learning (Active Learning) provides us with this possibility. ...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to: aim at the defects existing in the prior art, propose an active learning sample selection strategy that combines confidence and diversity, a...

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