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Modal-Regression-Based Broad Learning System for Robust Regression and Classification
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DOI：10.1109/TNNLS.2023.3256999.png)
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摘要
En 中文
A novel neural network, namely, broad learning system (BLS), has shown impressive performance on various regression and classification tasks. Nevertheless, most BLS models may suffer serious performance degradation for contaminated data, since they are derived under the least-squares criterion which is sensitive to noise and outliers. To enhance the model robustness, in this article we proposed a modal-regression-based BLS (MRBLS) to tackle the regression and classification tasks of data corrupted by noise and outliers. Specifically, modal regression is adopted to train the output weights instead of the minimum mean square error (MMSE) criterion. Moreover, the l(2,1)-norm-induced constraint is used to encourage row sparsity of the connection weight matrix and achieve feature selection. To effectively and efficiently train the network, the half-quadratic theory is used to optimize MRBLS. The validity and robustness of the proposed method are verified on various regression and classification datasets. The experimental results demonstrate that the proposed MRBLS achieves better performance than the existing state-of-the-art BLS methods in terms of both accuracy and robustness.
Keyword:
Robustness
Noise measurement
Feature extraction
Data models
Standards
Learning systems
Time series analysis
Broad learning system (BLS)
data regression
modal regression
pattern classification
sparsity prior
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
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