Anomaly Detection Algorithm Based on Broad Learning System and Support Vector Domain Description

Qun Huang, Zehua Zheng, Wenhao Zhu, Xiaozhao Fang, Ribo Fang and Weijun Sun ()
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Qun Huang: School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Zehua Zheng: School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Wenhao Zhu: School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Xiaozhao Fang: School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Ribo Fang: Huizhou Innovation Research Institute for Next Generation Industrial Internet, Huizhou 516000, China
Weijun Sun: School of Automation, Guangdong University of Technology, Guangzhou 510006, China

Mathematics, 2022, vol. 10, issue 18, 1-16

Abstract: Deep neural network-based autoencoders can effectively extract high-level abstract features with outstanding generalization performance but suffer from sparsity of extracted features, insufficient robustness, greedy training of each layer, and a lack of global optimization. In this study, the broad learning system (BLS) is improved to obtain a new model for data reconstruction. Support Vector Domain Description (SVDD) is one of the best-known one-class-classification methods used to solve problems where the proportion of sample categories of data is extremely unbalanced. The SVDD is sensitive to penalty parameters C, which represents the trade-off between sphere volume and the number of target data outside the sphere. The training process only considers normal samples, which leads to a low recall rate and weak generalization performance. To address these issues, we propose a BLS-based weighted SVDD algorithm (BLSW_SVDD), which introduces reconstruction error weights and a small number of anomalous samples when training the SVDD model, thus improving the robustness of the model. To evaluate the performance of BLSW_SVDD model, comparison experiments were conducted on the UCI dataset, and the experimental results showed that in terms of accuracy and F1 values, the algorithm has better performance advantages than the traditional and improved SVDD algorithms.

Keywords: broad learning system; data reconstruction; support vector domain description; anomaly detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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