Research Review for Broad Learning System: Algorithms, Theory, and Applications

In recent years, the appearance of the broad learning system (BLS) is poised to revolutionize conventional artificial intelligence methods. It represents a step toward building more efficient and effective machine-learning methods that can be extended to a broader range of necessary research fields....

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Vydáno v:IEEE transactions on cybernetics Ročník 52; číslo 9; s. 8922 - 8950
Hlavní autoři: Gong, Xinrong, Zhang, Tong, Chen, C. L. Philip, Liu, Zhulin
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.09.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Artificial intelligence
Biomedical engineering
Broad learning system (BLS)
classification
Computer architecture
Computer science
Computer vision
Data mining
feature learning
Knowledge engineering
Learning systems
Machine learning
Natural language processing
Neural networks
regression
research review
semisupervised learning (SSL)
Task analysis
Training
Unsupervised learning
ISSN:2168-2267, 2168-2275, 2168-2275
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Shrnutí:In recent years, the appearance of the broad learning system (BLS) is poised to revolutionize conventional artificial intelligence methods. It represents a step toward building more efficient and effective machine-learning methods that can be extended to a broader range of necessary research fields. In this survey, we provide a comprehensive overview of the BLS in data mining and neural networks for the first time, focusing on summarizing various BLS methods from the aspects of its algorithms, theories, applications, and future open research questions. First, we introduce the basic pattern of BLS manifestation, the universal approximation capability, and essence from the theoretical perspective. Furthermore, we focus on BLS's various improvements based on the current state of the theoretical research, which further improves its flexibility, stability, and accuracy under general or specific conditions, including classification, regression, semisupervised, and unsupervised tasks. Due to its remarkable efficiency, impressive generalization performance, and easy extendibility, BLS has been applied in different domains. Next, we illustrate BLS's practical advances, such as computer vision, biomedical engineering, control, and natural language processing. Finally, the future open research problems and promising directions for BLSs are pointed out.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2021.3061094