A novel LS-SVMs hyper-parameter selection based on particle swarm optimization

Guo, Xuexin; Yang, Jianbo; Wu, Chunguo; Wang, C. Y.; Liang, Yanchun

doi:10.1016/j.neucom.2008.04.027

Cited by 176 publications

(62 citation statements)

References 30 publications

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“…In order to automatize this process and to avoid an exhaustive or a random exploration of parameters, different authors have deployed search and optimization techniques [3,4,6,7,8,13,14,15]. In this context, the search space consists on a set of possible configurations of parameters and the objective function corresponds to a performance measure (e.g., precision estimated by cross-validation) obtained by the SVM on the problem.…”

Section: Svm Parameter Selectionmentioning

confidence: 99%

Combining meta-learning and search techniques to select parameters for support vector machines

et al. 2012

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Section: Svm Parameter Selectionmentioning

confidence: 99%

Combining meta-learning and search techniques to select parameters for support vector machines

et al. 2012

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“…Advantages of PSO based search are mainly the simplicity of the search process, ease of implementation that is demonstrated by the small number of parameters to be adjusted during initialization and search process. PSO also accomplishes an optimization task without mutation and cross-over operations, which are essential steps of GAs [17].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…This least square formulation [15,16] not only significantly reduces complexity of solving SVM learning problem but also provides an efficient mean for estimating the generalization ability of a nonlinear SVM classifier and for accomplishing feature selection using heuristic methods [17] more efficiently.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed PSO-LSSVM approach employs a particle swarm optimization (PSO) [18,19] heuristic search for accomplishing feature selection and for selecting the optimal parameters of a kernel-based LSSVM classifier. PSO and LSSVM techniques have been proven efficient for handling different optimization and pattern recognition problems [17]. Further, both LSSVM and PSO are simple and easy to implement, which offer tremendous computational savings compared to their counterparts GA and standard SVM, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Clustered Microcalcifications in Mammograms using Particle Swarm Optimization and Least-Squares Support Vector Machine

Zyout¹

2012

IJCA

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Feature selection and classifier hyper-parameter optimization are important stages of any computer-aided diagnosis (CADx) system for mammography. The optimal selection for shape features, kernel parameter, and classifier regularization constant is crucial to achieve a good generalization and performance of least-squares support vector machines (LSSVMs). This paper presents a morphology-based CADx that uses a computationally attractive and unified scheme for accomplishing the model selection task.A heuristic parameter search based on particle swarm optimization (PSO) not only reduces the dimensionality of the input feature space but also optimizes hyper-parameters of the classifier. The performance of the proposed shape-based CADx including PSO-LSSVM parameter selection method is examined using 60 microcalcification clusters. Using different cross-validation procedures, the proposed PSO-LSSVM demonstrated a good generalization ability by producing classification accuracies higher than 92%. The best classification accuracy of 97% was obtained using the leave-one-out cross-validation procedure. Comparing the performance of PSO-LSSVM with PSO-SVM method that uses conventional SVM formulation, results demonstrated the attractive computational complexity and classification performance of PSO-LSSVM.

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“…However, GA and SA are both complex. Peng proposed a selection method based on particle swarm algorithm (PSO) [9], which is easy to implement and has relatively high optimization efficiency. However, the drawback of PSO is that it may get trapped in local optima.…”

Section: Introductionmentioning

confidence: 99%

An Experimental Study of the Hyper-parameters Distribution Region and Its Optimization Method for Support Vector Machine with Gaussian Kernel

Yan¹,

Yang²,

Ding³

2013

IJSIP

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A novel LS-SVMs hyper-parameter selection based on particle swarm optimization

Cited by 176 publications

References 30 publications

Combining meta-learning and search techniques to select parameters for support vector machines

Combining meta-learning and search techniques to select parameters for support vector machines

Classification of Clustered Microcalcifications in Mammograms using Particle Swarm Optimization and Least-Squares Support Vector Machine

An Experimental Study of the Hyper-parameters Distribution Region and Its Optimization Method for Support Vector Machine with Gaussian Kernel

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