Research on Model and Algorithm of Multiview and Multilabel Classification Based on Nearest-Neighbor Model

In the field of machine learning, multilabel learning gradually evolved from the traditional text classification problem. In this study, a multiview multilabel via optimal classifier chain (MVMLOCC) algorithm based on the nearest-neighbor model is proposed. The algorithm model establishes a multilab...

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Vydáno v:Mathematical problems in engineering Ročník 2022; s. 1 - 10
Hlavní autoři: Zhang, Junyi, Rao, Yuan
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Hindawi 22.07.2022
John Wiley & Sons, Inc
Témata:
Algorithms
Artificial intelligence
Chains
Classification
Classifiers
Data collection
Datasets
Deep learning
Labeling
Machine learning
Mathematical problems
ISSN:1024-123X, 1563-5147
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Shrnutí:In the field of machine learning, multilabel learning gradually evolved from the traditional text classification problem. In this study, a multiview multilabel via optimal classifier chain (MVMLOCC) algorithm based on the nearest-neighbor model is proposed. The algorithm model establishes a multilabel chain classifier for each view of the dataset and predicts unknown data samples by dynamically adjusting the weights of chain learners. When an unknown example is input, multiple chain classifier models are multiplied by corresponding weights to obtain the final tag set situation. The model makes full use of the relevance between multiple tags and the complementarity and integrity between multiple perspectives and can achieve better learning results. The final experimental results show that our proposed model can be applied to different multilabel classification tasks and has achieved excellent performance under different evaluation indicators. This study mainly studies the multiview and multilabel classification method and uses active learning technology to solve the problem of high label components in data collection.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1024-123X
1563-5147
DOI:10.1155/2022/2056791