A genetic algorithm–support vector machine method with parameter optimization for selecting the tag SNPs

İlhan, İlhan; Tezel, Gülay

doi:10.1016/j.jbi.2012.12.002

Cited by 46 publications

(29 citation statements)

References 35 publications

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“…Miranda et al [19] proposed a prototype in which multi-objective PSO (MOPSO) and crowding distance mechanism (CDR) was used to select the values of two SVM parameters for classification problems. İlhan et al [20] optimized C and γ parameters of SVM by using PSO algorithm to predict single nucleotide polymorphisms (SNPs).…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

A Brief Overview on Parameter Optimization of Support Vector Machine

Cao¹,

Yu²,

Cheng³

2017

dtmse

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Support vector machine (SVM) has been successfully applied in classification and regression problems. But it is very sensitive to the selection of parameters. The fundamental principles of SVM are analyzed firstly. The main optimization methods and achievements for SVM parameters are introduced. And the popular fitness functions used for the parameter optimization of SVM are described. The objective of this paper is to provide readers a brief overview of the recent advances for parameter optimization of SVM and enable them to develop and implement new optimization strategies for SVM-related research at their disposal.

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Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

A Brief Overview on Parameter Optimization of Support Vector Machine

Cao¹,

Yu²,

Cheng³

2017

dtmse

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show abstract

“…To develop an efficient SVM model, C and r must be carefully predetermined. C determines the trade-offs between the minimization of the fitting error and minimization of the model complexity, and a is the bandwidth of the radial basis function (RBF) kernel (Wu et al 2007;Khazaee and Ebrahimzadeh 2010;Ilhan and Tezel 2013). The PSO method, based on an analogy with the collective motion of biological organisms, is considered a promising research tool worldwide (Lins et al 2012).…”

Section: Previous Workmentioning

confidence: 99%

Application of wavelet analysis and a particle swarm-optimized support vector machine to predict the displacement of the Shuping landslide in the Three Gorges, China

Ren

Zhang

et al. 2014

Environ Earth Sci

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Landslides occur frequently in the Three Gorges in China, posing threats to human life and the normal operation of the Three Gorges Dam. A number of preexisting landslides have been reactivated since the initial impoundment of the Three Gorges Reservoir in June 2003. An effective and accurate method of predicting landslide displacement is necessary to mitigate the effects of these disastrous landslides. This study carries out a landslide displacement prediction for the Shuping landslide using 7 years of monitoring data, wavelet analysis, and a particle swarm-optimized support vector machine (PSO-SVM) model. The landslide's displacement is strongly influenced by periodic precipitation and reservoir level fluctuations, and the cumulative displacement curve versus time indicates a step-like character. Based on the deformation characteristics of this landslide, the total displacement is divided into its trend and periodic components by means of the wavelet analysis. An S-curve estimation is used to predict the trend displacement via the curve fitting of the historical displacement versus time. Five primary factors are used as the input variables for a PSO-SVM model to predict periodic displacement. These factors include cumulative precipitation over the previous month, cumulative precipitation during a two-month period, maximum continuous decrement in the reservoir level during the current month, and cumulative increments and decrements in the reservoir level during the current month. The mean squared error, squared correlation coefficient, and Akaike's information criterion of the wavelet-PSO-SVM model at GPS monitoring points ZG85 and ZG87 are 2. 45, 0.945, and 20.80 and 10.46, 80.981, and 36.38, respectively. This method can be applied to the prediction of displacement in colluvial landslides in the Three Gorges. This study may provide useful information to engineers and planners involved in landslide prevention and reduction.

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“…Won et al [35] combined a novel GA-SVM to find features from biological sequences. A particle swarm optimization (PSO) [36] to optimize a GA-SVM method to predict single nucleotide polymorphisms (SNPs) and to select tag SNPs as pointed by Ilhan and Tezel [37] or to solve the heating system planning problem is presented [38]. Zhang et al combined PSO with SVM for classifying magnetic resonance imaging (MRI), brain images [39].…”

Section: State Of the Artmentioning

confidence: 99%

Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

Fernández-Lozano

Canto

Gestal

et al. 2013

The Scientific World Journal

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Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected.

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A genetic algorithm–support vector machine method with parameter optimization for selecting the tag SNPs

Cited by 46 publications

References 35 publications

A Brief Overview on Parameter Optimization of Support Vector Machine

A Brief Overview on Parameter Optimization of Support Vector Machine

Application of wavelet analysis and a particle swarm-optimized support vector machine to predict the displacement of the Shuping landslide in the Three Gorges, China

Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

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