A New Support Vector Machine Based on Convolution Product

Autor: Wei-Chang Yeh, Yunzhi Jiang, Shi-Yi Tan, Chih-Yen Yeh
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Complexity, Vol 2021 (2021)
Druh dokumentu: article
ISSN: 1076-2787
1099-0526
DOI: 10.1155/2021/9932292
Popis: The support vector machine (SVM) and deep learning (e.g., convolutional neural networks (CNNs)) are the two most famous algorithms in small and big data, respectively. Nonetheless, smaller datasets may be very important, costly, and not easy to obtain in a short time. This paper proposes a novel convolutional SVM (CSVM) that has the advantages of both CNN and SVM to improve the accuracy and effectiveness of mining smaller datasets. The proposed CSVM adapts the convolution product from CNN to learn new information hidden deeply in the datasets. In addition, it uses a modified simplified swarm optimization (SSO) to help train the CSVM to update classifiers, and then the traditional SVM is implemented as the fitness for the SSO to estimate the accuracy. To evaluate the performance of the proposed CSVM, experiments were conducted to test five well-known benchmark databases for the classification problem. Numerical experiments compared favorably with those obtained using SVM, 3-layer artificial NN (ANN), and 4-layer ANN. The results of these experiments verify that the proposed CSVM with the proposed SSO can effectively increase classification accuracy.
Databáze: Directory of Open Access Journals
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