Classifier construction method based on active learning

Abstract

The invention discloses a classifier construction method based on active learning. The current value and the prospective value of unlabeled samples are fully considered, so that samples with high value are selected. The method includes the steps that first, the current value of samples is calculated by using the optimal class information and the suboptimal class information of the samples first, and a part of samples with high value are selected to constitute a candidate sample set according to the high-low current value of the samples; afterwards, the prospective value of the samples in the candidate sample set is calculated, and the total value of the samples is obtained combined with the current value of the samples; finally, unlabeled samples with high value are selected to be labeled according to the total value of the samples, the unlabeled samples are added into a training sample set, and a classifier is updated. The experimental result on the basis of different data sets shows that the classifier with high classification accuracy can be obtained through the method under the condition that the samples of the same quantity are selected.

Description

Technical field [0001] The invention relates to a method for data classification by using a computer, in particular to a method for selecting and generating a training sample set from a large number of samples based on an active learning method, and constructing a trained data classifier. Background technique [0002] Automatic data classification is an important technology in computer automatic processing, which is widely used in data mining, medical diagnosis, traffic management, human body feature recognition and other fields. The data classification method in computer processing usually includes constructing a classifier model and training the classifier model with a training sample set to obtain a trained data classifier. [0003] In the data classification method, training the classifier model is the key difficulty, mainly because the classifier model requires users to label a large number of data training samples, and labeling a large number of data samples requires a lot of...

AI Technical Summary

Problems solved by technology

However, the BvSB algorithm only considers samples that are uncertain about the current classifier. In actual use, when samples are added to the training sample set, the uncertainty of the added samples will have an impact on the updated classifier, resulting in training results that cannot be achieved. expected goal

Therefore, the classifier constructed by the BvSB algorithm has certain defects

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