Determination of Optimal SVM Parameters by Using GA/PSO

Ren, Yuan; Bai, Guang-Chen

doi:10.4304/jcp.5.8.1160-1168

Cited by 89 publications

(53 citation statements)

References 7 publications

(11 reference statements)

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“…Recent research includes using genetic algorithm and particle swarm optimization to process parameter selection [3][4][5][6], using genetic algorithm to process gene selection in microarray to get better accuracy [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Parameter selection of support vector machines and genetic algorithm based on change area search

Ren

et al. 2011

Neural Comput & Applic

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Generalization performance of support vector machines (SVM) with Gaussian kernel is influenced by its model parameters, both the error penalty parameter and the Gaussian kernel parameter. After researching the characteristics and properties of the parameter simultaneous variation of support vector machines with Gaussian kernel by the parameter analysis table, a new area distribution model is proposed, which consists of optimal straight line, reference point of area boundary, optimal area, transition area, underfitting area, and overfitting area. In order to improve classification performance of support vector machines, a genetic algorithm based on change area search is proposed. Comparison experiments show that the test accuracy of the genetic algorithm based on change area search is better than that of the two-linear search method.

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Section: Introductionmentioning

confidence: 99%

Parameter selection of support vector machines and genetic algorithm based on change area search

Ren

et al. 2011

Neural Comput & Applic

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“…Also, Pain and Hong in [5], [6] presented a simulated annealing approach to obtain parameter values of SVM and test their approach on a real data set. Ren and Bai [7] developed an approach to determine the optimal SVM parameters by using genetic algorithms and particle swarm optimization. These studies focused only on the determination of the parameters.…”

Section: Introductionmentioning

confidence: 99%

Microcanonical Annealing and Threshold Accepting for Parameter Determination and Feature Selection of Support Vector Machines

Medjahed

Saadi

Benyettou

et al. 2016

CIT. J. comput. inf. technol

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Support vector machine (SVM) is a popular classification technique with many diverse applications. Parameter determination and feature selection significantly influences the classification accuracy rate and the SVM model quality. This paper proposes two novel approaches based on: Microcanonical Annealing (MA-SVM) and Threshold Accepting (TA-SVM) to determine the optimal value parameter and the relevant features subset, without reducing SVM classification accuracy. In order to evaluate the performance of MA-SVM and TA-SVM, several public datasets are employed to compute the classification accuracy rate. The proposed approaches were tested in the context of medical diagnosis. Also, we tested the approaches on DNA microarray datasets used for cancer diagnosis. The results obtained by the MA-SVM and TA-SVM algorithms are shown to be superior and have given a good performance in the DNA microarray data sets which are characterized by the large number of features. Therefore, the MA-SVM and TA-SVM approaches are well suited for parameter determination and feature selection in SVM.

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“…The parameters that should be optimized are the complexity parameter C, epsilon ε and tolerance t and the kernel function parameters, such as γ for Gaussian kernel. The parameter C determines the trade-off between the fitting error minimization and model complexity [37,29,9,24], where a bad choice of C leads to an imbalance between model complexity minimization and empirical risk minimization. The last two parameters ε, where its value indicates the error expectation in the classification process of the sample data, and it impacts the number of support vectors generated by the classifier [24], while t, is the tolerance parameter.…”

Section: Description About Datasetmentioning

confidence: 99%

SS-SVM (3SVM): A New Classification Method for Hepatitis Disease Diagnosis

Afif¹,

Hedar²,

Hamid³

et al. 2013

IJACSA

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Abstract-In this paper, a new classification approach combining support vector machine with scatter search approach for hepatitis disease diagnosis is presented, called 3SVM. The scatter search approach is used to find near optimal values of SVM parameters and its kernel parameters. The hepatitis dataset is obtained from UCI. Experimental results and comparisons prove that the 3SVM gives better outcomes and has a competitive performance relative to other published methods found in literature, where the average accuracy rate obtained is 98.75%.

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Determination of Optimal SVM Parameters by Using GA/PSO

Cited by 89 publications

References 7 publications

Parameter selection of support vector machines and genetic algorithm based on change area search

Parameter selection of support vector machines and genetic algorithm based on change area search

Microcanonical Annealing and Threshold Accepting for Parameter Determination and Feature Selection of Support Vector Machines

SS-SVM (3SVM): A New Classification Method for Hepatitis Disease Diagnosis

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