Classification model training method based on crowdsourcing technology

Abstract

The invention provides a classification model training method based on the crowdsourcing technology. The method comprises a step of estimating a level of providing annotation information of a user on crowdsourcing annotation information corresponding to a few samples, a step of taking an observed annotation people level as prior knowledge to determine the annotation information used by training samples, a step of taining a classification model on the training sample and the annotation information, a step of using the classification model to select a training sample which allows a model expected error to be minimum, and predicting a category to which the sample belongs, a step of adding the selected sample and annotation information provided by a user with a highest annotation level in the category into a training set, and a step of carrying out iterative execution of the above steps on an updated training set until the precision of the classification model or the number of the training samples reaches a preset standard. The method has the advantages that disadvantageous influence of the low quality annotation information provided by a user with a low annotation level on the classification model training is avoided, and an effect of training a high generalization ability classification model in a crowdsourcing environment is guaranteed.

Description

technical field [0001] The invention relates to a classification model training method. Background technique [0002] Currently, under the framework of supervised learning in machine learning, training a classification model requires pre-collecting a set of data samples with labeled information. The quantity and quality of the collected training data directly determine the generalization performance of the classification model. In the traditional training data collection process, experts with professional domain knowledge are required to provide unique and correct labeling information corresponding to data samples to ensure that the trained classification model has good generalization performance. [0003] The challenge of this traditional approach is that there are fewer people with professional backgrounds in real-world tasks, and it takes a lot of time and time to obtain sample labeling information. Therefore, with the development of network technology and data storage ...

AI Technical Summary

Problems solved by technology

Since the annotation information obtained based on crowdsourcing technology comes from multiple online network users, it is difficult to guarantee the quality of the collected annotation information. experience and its accuracy in completing labeling tasks

Directly using crowdsourced annotation information to train the classification model will seriously affect the generalization performance of the classification model

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