Sorting method and system for active learning

Abstract

The invention discloses a sorting method and system for active learning. The sorting method comprises the following steps: firstly sorting all samples with relatively high uncertainty from original unmarked samples in a concentrated manner so as to obtain a most uncertain sample set; then dividing the most uncertain sample set into h different clusters by utilizing the similarity among the samples, dividing the samples with the relatively high similarity into the same cluster, and screening out most representative samples in each cluster to form the most representative sample set; and subsequently, carrying out information marking on the most representative sample set, and finally realizing the sorting of target objects by utilizing a marked sample train sorter and a trained analyzer. Therefore, the sorting method and system disclosed by the invention has the advantages that by clustering, the similar and relatively-high-redundancy samples are divided into the same type, and the screening is carried out on the basis of the clustering, so that the redundancy among the finally-selected samples to be marked is avoided, the marking time and workload are reduced and the sorting efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of machine learning (Machine Learning) classification, and in particular relates to an active learning classification method and system. Background technique [0002] Information classification is a very important issue in the fields of information processing and pattern recognition, and the research on classification methods has become a hot topic in current research. The key issue for each classification method to achieve classification is to train a classifier with high classification accuracy based on labeled samples. [0003] The classification accuracy of a classifier largely depends on the labeled sample set. In reality, the cost of labeling samples and obtaining labeled samples is high (manual labeling by domain experts is required). Therefore, in order to obtain high classification accuracy at the lowest possible labeling cost, it is necessary to start from the original unidentified samples. The s...

AI Technical Summary

Problems solved by technology

[0004] The above method only considers the uncertainty and representativeness of the samples, but does not consider the redundancy between the screened samples, resulting in high redundancy between the screened samples, and labeling redundant samples is time-consuming and laborious. And because the information it contains is redundant information and will not help the training of the classifier, the above method has the disadvantage of high redundancy among the selected samples, which increases the time and cost of labeling, and is time-consuming and laborious. thereby reducing the classification efficiency

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