Classification method based on the deep structure and least squares support vector machine

• Published in: Electronics letters 2020-05, Vol.56 (11), p.538-541
• Main Authors: Ma, Wenlu, Liu, Han
• Format: Article
• Language: English
• Subjects: classification method; classification problem; Control engineering; deep structure; density‐dependent quantised LSSVM methods; discriminant classification function; edge detection; good generalisation abilities; large‐scale data sets; learning (artificial intelligence); least squares approximations; least squares support vector machine; LSSVM model; multilayer SVM; pattern classification; representative shallow network models; smaller sample set; support vector machines; UCI data sets
• ISSN: 0013-5194; 1350-911X; 1350-911X
• DOI: 10.1049/el.2019.3776

Support vector machines (SVMs) are one of the most representative shallow network models and have good generalisation abilities in small data sets. In this Letter, a new classification method based on the deep structure and least squares SVM (LSSVM) is proposed. For large-scale data sets, the method builds the structures of a multi-layer SVM. Using edge detection and the K-means algorithm, the sample set is compressed into a smaller sample set, which is used to train the LSSVM model of each layer and the discriminant classification function is obtained. Finally, this method is applied to UCI data sets and compared with several density-dependent quantised LSSVM methods and other methods. The experimental results show that the method has good performance in solving the large-scale data set classification problem.