Sample classification method based on active learning and neural network

A neural network and active learning technology, applied in the field of machine learning, can solve the problems of incomplete measurement of uncertainty, low model classification accuracy, etc., to reduce the time and money cost, improve the classification effect, and reduce the number of effects.

Inactive Publication Date: 2021-09-03
XIANGTAN UNIV
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AI Technical Summary

Problems solved by technology

[0005] The above three uncertain screening strategies each represent a part of the uncertainty of the label of the model to the sample, and cannot completely measure the uncertainty of the model to the sample. In practical applications, the classification accuracy of the model is relatively low.

Method used

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  • Sample classification method based on active learning and neural network
  • Sample classification method based on active learning and neural network
  • Sample classification method based on active learning and neural network

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Embodiment Construction

[0020] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0021] Taking a three-layer feed-forward neural network model as an example, the active learning sample screening method according to the present invention is described. However, those skilled in the art should understand that the present invention is not limited to this neural network, but can be applied to other multi-classification neural networks whose output uses Softmax.

[0022] figure 1 The three-layer feed-forward neural network model shown is a model for multi-sample classification tasks. Taking sample x as an example, the input layer is the 0th layer, which represents the feature vector of sample x, the middle two layers are hidden layers, and the output The number n of layer nodes represents the number of labels that sample x may take, w (i) Represents the connection weight matrix between the i-1th layer and the i-th lay...

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Abstract

The invention discloses a sample classification method based on active learning and a neural network, and belongs to the field of machine learning in intelligent science and technology. According to the method, the uncertainty of a neural network model to sample points is taken as a reference, three traditional uncertainty indexes of Least content, Margin and Entry are calculated respectively, the three indexes are used for voting samples, the sample with the highest vote number is the finally screened sample points, the sample points are the most uncertain samples of the model, and then training of the neural network model is facilitated. The number of sample points needing to be marked can be effectively reduced, the marking cost is reduced, and the classification precision of the model is improved.

Description

technical field [0001] The invention belongs to the technical field of machine learning in intelligence science and technology. Specifically, it involves a method based on neural network, introducing active learning and using three types of uncertainty indicators to vote samples, manually marking samples with high votes, constructing high-quality training sample sets, and further improving the classification accuracy of neural networks. Background technique [0002] The learning of traditional neural network models is mainly based on supervised learning. This learning method requires a large number of labeled samples to guide the model for training, so as to continuously improve the performance of the model in the target field, and finally obtain a certain generalization ability and put it into practical application scenarios. middle. However, the labeling of training samples is generally done by experts in related fields, and obtaining high-quality labels requires a lot of...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08G06K9/62
CPCG06N3/08G06N3/047G06F18/2415
Inventor 周友行孟高磊赵文杰易倩沈旺
Owner XIANGTAN UNIV
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