Abstract
Broad Learning System (BLS), a type of neural network with a non-iterative training mechanism and adaptive network structure, has attracted much attention in recent years. In BLS, since the mapped features are obtained by mapping the training data based on a set of random weights, their quality is unstable, which in turn leads to the instability of the generalization ability of the model. To improve the diversity and stability of mapped features in BLS, we propose the BLS with Hybrid Features (BLSHF) algorithm in this study. Unlike original BLS, which uses a single uniform distribution to assign random values for the input weights of mapped feature nodes, BLSHF uses different distributions to initialize the mapped feature nodes in each group, thereby increasing the diversity of mapped features. This method enables BLSHF to extract high-level features from the original data better than the original BLS and further improves the feature extraction effect of the subsequent enhancement layer. Diverse features are beneficial to algorithms that use non-iterative training mechanisms, so BLSHF can achieve better generalization ability than BLS. We apply BLSHF to solve the problem of air quality evaluation, and the relevant experimental results empirically prove the effectiveness of this method. The learning mechanism of BLSHF can be easily applied to BLS and its variants to improve their generalization ability, which makes it have good application value.
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Acknowledgment
This work was supported by National Natural Science Foundation of China (Grant No. 62106150), CAAC Key Laboratory of Civil Aviation Wide Surveillance and Safety Operation Management and Control Technology (Grant No. 202102), and CCF-NSFOCUS (Grant No. 2021001).
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Cao, W., Li, D., Zhang, X., Qiu, M., Liu, Y. (2022). BLSHF: Broad Learning System with Hybrid Features. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13369. Springer, Cham. https://doi.org/10.1007/978-3-031-10986-7_53
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