A Classification Algorithm for Network Traffic based on Improved Support Vector Machine

Ding, Lei; Yu, Fei; Sheng, Ping; Chen, Xu

doi:10.4304/jcp.8.4.1090-1096

Cited by 17 publications

(10 citation statements)

References 16 publications

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“…where 1 c and 2 c are penalty parameters, (1) ξ and (2) ξ are slack vectors, 1 e and 2 e are vectors of ones of appropriate dimensions.…”

Section: Polynomial Smooth Twin Support Vector Machinesmentioning

confidence: 99%

See 1 more Smart Citation

Polynomial Smooth Twin Support Vector Machines Based on Invasive Weed Optimization Algorithm

Ding¹,

Huang²,

Yu³

et al. 2014

JCP

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Smoothing functions can transform the unsmooth twin support vector machines (TWSVM) into smooth ones, and thus better classification results can be obtained. It has been one of the key problems to seek a better smoothing function in this field for a long time. In this paper, a novel version for smooth TWSVM, termed polynomial smooth twin support vector machines (PSTWSVM), is proposed. In PSTWSVM, using the series expansion, a new class of polynomial smoothing is proposed, and then their important properties are discussed. It is shown that the approximation accuracy and smoothness rank of polynomial functions can be as high as required. Subsequently, the polynomial functions are used to convert the original constrained quadratic programming problems of TWSVM into unconstrained minimization problems, and then are solved by the well-known Newton-Armijo algorithm. Meanwhile, in order to find the suitable parameters of PSTWSVM, Invasive Weed Optimization (IWO) algorithm is used to optimize the proposed algorithm. Then we propose an algorithm called polynomial smooth twin support vector machines based on invasive weed optimization algorithm (PSTWSVM-IWO). Finally, the effectiveness of the proposed method is demonstrated via experiments on synthetic and UCI benchmark datasets.

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“…where 1 c and 2 c are penalty parameters, (1) ξ and (2) ξ are slack vectors, 1 e and 2 e are vectors of ones of appropriate dimensions.…”

Section: Polynomial Smooth Twin Support Vector Machinesmentioning

confidence: 99%

“…Support vector machine (SVM) proposed by Vapnik and co-worker [1] is a computationally powerful kernel-based tool for binary data classification and regression. Because the theory of SVM is based on the idea of structural risk minimization principle, SVM has successfully solved the high dimensionality and local minimum problems.…”

Section: Introductionmentioning

confidence: 99%

Polynomial Smooth Twin Support Vector Machines Based on Invasive Weed Optimization Algorithm

Ding¹,

Huang²,

Yu³

et al. 2014

JCP

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show abstract

“…Firstly proposed by Vapnik et al, Support Vector Machine (SVM) is a machine learning method which is applied to solve the binary classification problem [1][2][3]. It is an algorithm based on the VC dimension theory and the principle of structural risk minimization in the statistical learning theory,and it has the features of optimization, nuclear and the best generalization ability [4,5].The majority of the scholars have been concerned about it and it has been applied in many fields in recent years [6][7][8][9][10][11]. The majority of researchers have proposed many improved algorithms on the basis of SVM.…”

Section: Introductionmentioning

confidence: 99%

Mixed Kernel Twin Support Vector Machines Based on the Shuffled Frog Leaping Algorithm

Wu¹,

Ding²,

Huang³

et al. 2014

JCP

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The efficiency and performance of Twin Support Vector Machines (TWSVM) is better than the traditional support vector machines when it deals with the problems. However, it also has some problems. As the same as the traditional support vector machines, its parameters are difficult to be appointed and it is not easy to select the appropriate kernel function. TWSVM generally selects the Gaussian radial basis kernel function. Although its learning ability is very strong, its generalization ability is relatively weak. To a certain extent, this limits the performance of TWSVM .In order to solve these two problems, in this paper, we propose the Mixed Kernel Twin Support Vector Machines based on the shuffled frog leaping algorithm (SFLA-MK-TWSVM). To make full use of both the excellent generalization ability of global kernel functions and the learning ability of local kernel functions, SFLA-MK-TWSVM constructs a mixed kernel which has better performance. Then SFLA-MK-TWSVM uses the shuffled frog leaping algorithm to determine the parameters of both TWSVM and the mixed kernel function to further improve the performance of TWSVM. The experimental results indicate that SFLA-MK-TWSVM significantly improves the classification accuracy of TWSVM.Index Terms-the mixed kernel function, TWSVM, the shuffled frog leaping function

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“…It was firstly proposed by Vapnik [3]et al It is an algorithm which is based on the VC dimension theory and the principle of structural risk minimization in the statistical learning theory [4,5] .It has the features of optimization, nuclear and the best generalization ability. In recent years, it has attracted the attention of the majority of scholars and been used in many fields [6][7][8][9][10][11]. On the basis of SVM, The majority of researchers have proposed many improved algorithms.…”

Section: Introductionmentioning

confidence: 99%

Twin Support Vector Machines Based on Quantum Particle Swarm Optimization

Ding¹,

Wu²,

Nie³

et al. 2013

JSW

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Twin Support Vector Machines (TWSVM) are developed on the basis of Proximal Support Vector Machines (PSVM) and Proximal Support Vector Machine based on the generalized eigenvalues(GEPSVM). The solving of binary classification problem is converted to the solving of two smaller quadratic programming problems by TWSVM. And then it gets two non-parallel hyperplanes. Its efficiency of dealing with the problems and performance are better than the traditional support vector machines. However, it also has some problems. Its own parameters are difficult to be appointed. In order to solve this problem, on the basis of in-depth study of TWSVM, this paper proposes an algorithm that is the Twin Support Vector Machines based on Quantum Particle Swarm Optimization (QPSO-TWSVM) .By the use of the global searching ability of the Quantum Particle Swarm Optimization (QPSO), QPSO-TWSVM can search the optimal parameters in the global scope and avoid itself falling into the local optimum prematurely to find the values of the parameters which are the closest to the optimal parameters. QPSO-TWSVM avoids using the empirical values to appoint the parameters successfully. Compared with the traditional TWSVM, QPSO-TWSVM can appoint the parameters more accurately and avoid selecting the parameters blindly. Because of the better parameter selections, QPSO-TWSVM improves the classification accuracy of TWSVM.

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A Classification Algorithm for Network Traffic based on Improved Support Vector Machine

Cited by 17 publications

References 16 publications

Polynomial Smooth Twin Support Vector Machines Based on Invasive Weed Optimization Algorithm

Polynomial Smooth Twin Support Vector Machines Based on Invasive Weed Optimization Algorithm

Mixed Kernel Twin Support Vector Machines Based on the Shuffled Frog Leaping Algorithm

Twin Support Vector Machines Based on Quantum Particle Swarm Optimization

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