Three-way decision active learning method taking neighborhood entropy as query strategy

The invention discloses a three-way decision active learning method taking neighborhood entropy as a query strategy. The method comprises the following steps: training a classifier by using a marked data set; classifying the test set by using the trained classifier and recording a classification result of the test set; calculating neighborhood entropies of all the unmarked data, and dividing the unmarked data into a positive domain, a boundary domain and a negative domain according to the size of the neighborhood entropies; respectively processing the data of different areas; selecting a part of most valuable unmarked data, and marking the unmarked data by a human expert or an annotation device; after marking, adding a marked data set and using the marked data set for the next training of the classifier; and executing the above processes in a loop iteration manner until a preset condition or an expected evaluation standard is reached, and stopping learning. According to the method, a small amount of most valu