Label Distribution Learning Based on Two-stage Model Error Repair Method

Label distribution learning (LDL) is a new learning paradigm proposed in the field of machine learning to solve the problem of label ambiguity. Many existing label distribution learning algorithms only consider the simple relationship between labels and do not mine out the complex relationship betwe...

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Bibliographic Details
Published in2023 13th International Conference on Information Technology in Medicine and Education (ITME) pp. 786 - 790
Main Authors Mao, Yu, Cai, Zhiyi, Li, Yulin, Shi, Chunyu
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.11.2023
Subjects
additional features
error repair
label distribution learning
Machine learning
Machine learning algorithms
Maintenance engineering
Prediction algorithms
Predictive models
Size measurement
Training
two-stage model
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Summary:Label distribution learning (LDL) is a new learning paradigm proposed in the field of machine learning to solve the problem of label ambiguity. Many existing label distribution learning algorithms only consider the simple relationship between labels and do not mine out the complex relationship between labels. To address this problem, this paper proposes a label distribution learning based on a two-stage model error repair method(LDL-TERM). First, the initial model is used to predict the training set, and its prediction results are used as additional Features are integrated into the LDL-TER model; secondly, norm regularization is used to control the relative importance between the additional feature set and the original feature set. Experiments on 14 related data sets prove that the algorithm has better performance.
ISSN:2474-3828
DOI:10.1109/ITME60234.2023.00161