A semi-supervised crowdsourced labeling data integration method for label imbalance

Abstract

The invention discloses a semi-supervised crowdsourcing labeling data integration method for label imbalance, which is based on the following two phenomena: (1) the labeling accuracy rate of the labeler has nothing to do with the object; The weights considered in different labeling tasks of the object are the same; a new evaluation method of weighted parameters and the evaluation method of the labeler's ability are proposed, and a semi-supervised crowdsourcing labeling data integration method for label imbalance is constructed, using an iterative method Solve, so that the evaluation of weighted parameters and the ability of the labeler is more objective and accurate, and the integrated labeling results are more accurate; at the same time, the present invention is applicable to various types of crowdsourcing labeling data, including but not limited to: data such as images, texts, and videos form of multi-category labeling, etc.

Description

technical field [0001] The invention belongs to the technical field of data labeling, and in particular relates to a semi-supervised crowdsourcing labeling data integration method oriented to label imbalance. The method comprehensively considers weighting parameters and labeler capabilities. Background technique [0002] With the advent of the big data era, extracting knowledge from big data is the most important research direction in the computer field today, attracting the attention of fields such as artificial intelligence and machine learning. Methods such as machine learning rely on high-quality labeled datasets for algorithm and model training. Therefore, it is of great significance to quickly and efficiently construct high-quality data sets. In the past, the construction of datasets mainly relied on expert labeling, through the way of hiring, after a period of high-intensity work to manually label the data. This method has the characteristics of high quality, high c...

AI Technical Summary

Problems solved by technology

Although the above algorithm improves the integration accuracy of labeled data to a certain extent, in the process, when considering the final result integration, it is assumed that each label type has the same probability of being labeled.

However, in actual labeling, the types of tags are often unbalanced, and there are certain weight differences between them. It is necessary to introduce weighted parameters to rebalance the relationship between tags.

At the same time, the weights in different labeling tasks must be different, which can only be trained in actual tasks and cannot be pre-set

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