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Multi-label active learning classification method and system based on SVM

A technology of active learning and classification methods, applied in the field of machine learning, can solve problems such as inapplicable multi-label samples

Active Publication Date: 2014-07-16
SUZHOU UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In view of this, this application provides a multi-label active learning classification method and system based on SVM, which is used to solve the problem that the existing active learning classification methods are not suitable for multi-label samples

Method used

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  • Multi-label active learning classification method and system based on SVM
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  • Multi-label active learning classification method and system based on SVM

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Experimental program
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Embodiment 1

[0061] Embodiment 1 of the present invention discloses a multi-label active learning classification method based on SVM, see figure 1 as shown, figure 1 It is a flowchart of an SVM-based multi-label active learning classification method disclosed in Embodiment 1 of the present invention. The method includes:

[0062] S101: Construct a candidate sample set.

[0063] In this step, the candidate sample set is specifically a set of samples selected based on the Max-Margin uncertainty sampling strategy, where Max-Margin uncertainty is a sampling strategy based on uncertainty, and the uncertainty sampling strategy is to use The trained classifier classifies the samples, and selects those samples with high uncertainty through a certain selection criterion.

[0064] like figure 2 as shown, figure 2 It is a flow chart of constructing a candidate sample set disclosed in Embodiment 1 of the present invention. include:

[0065] S201: Train part of the training samples to obtain a...

Embodiment 2

[0112] Embodiment 2 of the present invention discloses a multi-label active learning classification system based on SVM, see Figure 4 as shown, Figure 4 It is a schematic structural diagram of an SVM-based multi-label active learning classification system disclosed in Embodiment 2 of the present invention. The system includes: a construction unit 401, a determination unit 402, a labeling unit 403, an update unit 404 and a classification unit 405, wherein:

[0113] A construction unit 401, configured to construct a candidate sample set.

[0114] It should be noted that the construction unit 401 specifically uses the samples selected based on the Max-Margin uncertainty sampling strategy to construct the candidate sample set. Among them, Max-Margin uncertainty is a sampling strategy based on uncertainty. The uncertainty sampling strategy is to use the trained classifier to classify samples, and select those with high uncertainty through a certain selection standard. sample. ...

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Abstract

The invention discloses a multi-label active learning classification method and system based on SVM. The method includes the steps that a candidate sample set is established; a label set to which all samples in the candidate sample set belong is determined; responding to user operation, the candidate sample set and the label set to which all the samples in the candidate sample set belong are labeled, and then the labeled samples are obtained;the labeled samples are added to a training sample set for training, and a classifier is updated; the classifier is utilized to classify the acquired samples to be classified. According to the multi-label active learning classification method based on SVM, the label set to which the samples belong is preliminarily determined, labor cost and manually-labeled time are saved to a large degree, and multi-label sample learning classification is achieved on the basis of saving labor.

Description

technical field [0001] The present application relates to the technical field of machine learning, and more specifically, relates to an SVM-based multi-label active learning classification method and system. Background technique [0002] With the advent of the information age, a large amount of information has begun to exist in a computer-readable form, and the amount has increased dramatically. However, the information is mixed, and many meaningful data are submerged by a large amount of junk information. How to automatically classify useful information from these information will be an important topic. [0003] In the existing classification problems, it is assumed that a sample belongs to only one class label, and then the sample is classified by using a support vector machine through a supervised learning method. Support Vector Machine (SVM, Support Vector Machine) is a statistical learning method that has been widely used since the 1990s. It is a new classification tec...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06K9/62
CPCG06N20/00G06F18/2411
Inventor 赵朋朋焦阳鲜学丰吴健崔志明
Owner SUZHOU UNIV