Open AccessDouble-layer Support Vector Machine for Robust and Efficient Classification
Author(s) -
Yuting Wu,
Yigang He,
Luqiang Shi,
Yongbo Sui,
Hongxing Yuan,
Tongtong Cheng
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1302/3/032052
Subject(s) - support vector machine , relevance vector machine , structured support vector machine , computer science , least squares support vector machine , pattern recognition (psychology) , binary classification , artificial intelligence , binary number , computation , entropy (arrow of time) , classifier (uml) , algorithm , data mining , mathematics , physics , arithmetic , quantum mechanics
In this paper, double-layer support vector machine (DLSVM) for binary classification is proposed that can expeditiously learn a classification model under the premise of ensuring accuracy. In the first layer, least squares support vector machine (LSSVM) is used to evaluate all samples in the data set. Support vector machine (SVM) utilize a sparse data set to train the binary classifier in the second layer. In order to obtain excellent sparse data set for SVM, the normalized norm sequence and the normalized Lagrange multiplier Shannon entropy sequence are introduced. DLSVM provides low computation cost because the two-layer structure reduces lots of iterative operations. Experimental results show that the proposed method not only has similar classification accuracy compared to SVM but also has higher efficiency.