Text classification using a new classification model: L1-LS-SVM

DOI

10.2991/icmia-16.2016.66How to use a DOI?

Keywords

LS-SVM; SVM; L1-LS-SVM; Text classification

Abstract

With the advent of big-data age, it is essential to organize, analyze, retrieve and protect the useful data or sensitive information in a fast and efficient way for customers from different industries and fields. Least squares support vector machine (LS-SVM) has an outstanding advantage of lower computational complexity than that of standard support vector machines. Its shortcomings are the loss of sparseness and robustness. Thus it usually results in slow testing speed and poor generalization performance. In this paper, a least squares support vector machine with L1 penalty (L1-LS-SVM) is proposed to deal with above shortcomings. A minimum of 1-norm based object function is chosen to get the sparse and robust solution based on the idea of basis pursuit (BP) in the whole feasibility region. A real Chinese corpus from Fudan University is used to demonstrate the effectiveness of this model. The experimental results show that L1-LS-SVM can obtain a small number of support vectors and improve the generalization ability of LS-SVM.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Liwei Wei
AU  - Chuanshen Wei
AU  - Qiang Xiao
AU  - Ying Zhang
PY  - 2016/11
DA  - 2016/11
TI  - Text classification using a new classification model: L1-LS-SVM
BT  - Proceedings of the 2016 5th International Conference on Measurement, Instrumentation and Automation (ICMIA 2016)
PB  - Atlantis Press
SP  - 370
EP  - 375
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmia-16.2016.66
DO  - 10.2991/icmia-16.2016.66
ID  - Wei2016/11
ER  -