Abstract—Twin support vector machines (TWSVM) is similar in spirit to proximal SVM based on generalized eigenvalues (GEPSVM), which constructs two nonparallel planes by solving two related SVM-type problems, so that its computing cost in the training phase is only 1/4 of standard SVM. In addition to keeping the advantages of GEPSVM, the classification performance of TWSVM is also significantly better than that of GEPSVM. However, there are also many deficiencies in TWSVM, difficult to specify the parameters is one of them, in order to overcome this deficiency, in this paper, we propose the twin support vector machines based on particle swarm optimization (PSO- TWSVM). This algorithm use PSO to find the parameters for TWSVM, so that blindly parameters selection is avoided. The experimental results show that this algorithm is able to find the suitable parameters, and has higher classification accuracy compared with some other algorithms.
Index Terms—Twin Support Vector Machines; Particle Swarm Optimization; Pattern classification; Parameter optimization
I. INTRODUCTION
Support vector machines (SVM) [1-2] is a new machine learning method based on statistical learning theory and structural risk minimization [3-4], and it has become a hot research topic in the field of machine learning because of its excellent performance. In order to reduce the computational cost of SVM, Fung et al. [5] proposed proximal support vector machines (PSVM) in 2001, does binary classification by obtaining two parallel hyperplanes on the premise of guaranteeing the maximum interval. In 2006, Mangasarian and Wild [6] proposed proximal SVM based on generalized eigenvalues (GEPSVM), which successfully overcomes the existing shortcomings of PSVM. This algorithm abandons the constraint of PSVM that hyperplanes must be parallel. The optimization target of it is that each hyperplane should be as close as possible to the samples for its own
Manuscript received November 1, 2012; revised December 1, 2012;
accepted January 1, 2012.
Corresponding author: Shifei Ding, Email: dingsf@cumt.edu.cn
class and as far as possible from the samples for the other class at the same time. Jayadeva et al. [7] proposed twin support vector machines (TWSVM) [8] in 2007, as a variant of GEPSVM, attempts to improve the generalization of GEPSVM, its thought is to solve two dual quadratic programming problems(QPPs) of smaller size rather than solving one dual quadratic programming problem with large number of parameters in standard SVM. Compared with SVM, one of the main advantages of TWSVM is that it is four times faster. The classification performance of TWSVM also is better than GEPSVM, and it is very powerful to deal with large-scale datasets, while the standard SVM is not suitable for a large number of samples. However, it is inevitable for TWSVM to solve two QPPs that lead to rather high computational complexity.
Although TWSVM is proposed only recently, it has become a hot research topic because of its solid theoretical and practical foundation. Many scholars devote themselves to the study of TWSVM [9-10] and propose some improved algorithms. For example, Jing Chen and Ji Guangrong [11] proposed weighted least squares twin support vector machines (WLSTWSVM)， in order to eliminate the impact of noise and obtain better classification performance[12-13], different weights are put on the error variables. Ye Qiaolin et al. [14] proposed weighted twin support vector machines with local information (WLTWSVM) which is a new nonparallel plane classifier. It can mine as much correlation between data points with the same labels that may be important for classification performance as possible. WLTSVM can not only get better classification accuracy, but also reduce the computational cost. Qi Zhiquan et al. [15] proposed a new robust twin support vector machines (RTWSVM) via second order cone programming formulations for classification. This algorithm can deal with data with measurement noise efficiently.
TWSVM is widely used in various fields and get impressive experimental results because of its high classification accuracy and speed. For example, Ganesh R. Naik [16] and SP Arjunan [17] applied TWSVM to the gesture classification based on sEMG, and the result shows that it is eminently suited to such applications.
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