Particle swarm optimization for parameter determination and feature selection of support vector machines

Lin, Shih-Wei; Ying, Kuo‐Ching; Chen, Shih‐Chieh; Lee, Zne-Jung

doi:10.1016/j.eswa.2007.08.088

Cited by 795 publications

(343 citation statements)

References 25 publications

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“…This happens when searching the optimal pair value on polynomial kernel that requires computation time is relatively longer than other kernels, since this kernel has four parameters that are necessary to be optimized. While, if too small, a search region might render a satisfactory outcome impossible [43]. Therefore, we have precisely analyzed the search region first before performing the optimization simulation.…”

Section: Performance Evaluationmentioning

confidence: 99%

A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

et al. 2018

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Abstract:Termites are the most destructive pests and their attacks significantly impact the quality of wooden buildings. Due to their cryptic behavior, it is rarely apparent from visual observation that a termite infestation is active and that wood damage is occurring. Based on the phenomenon of acoustic signals generated by termites when attacking wood, we proposed a practical framework to detect termites nondestructively, i.e., by using the acoustic signals extraction. This method has the pros to maintain the quality of wood products and prevent higher termite attacks. In this work, we inserted 220 subterranean termites into a pine wood for feeding activity and monitored its acoustic signal. The two acoustic features (i.e., energy and entropy) derived from the time domain were used for this study's analysis. Furthermore, the support vector machine (SVM) algorithm with different kernel functions (i.e., linear, radial basis function, sigmoid and polynomial) were employed to recognize the termites' acoustic signal. In addition, the area under a receiver operating characteristic curve (AUC) was also adopted to analyze and improve the performance results. Based on the numerical analysis, the SVM with polynomial kernel function achieves the best classification accuracy of 0.9188.

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Section: Performance Evaluationmentioning

confidence: 99%

A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

et al. 2018

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“…Moreover, binary PSO [225], which is a discrete optimization method was employed for selecting the input features which were binary coded [226,227]. In [226], a modified version of binary PSO was proposed where EA like mutation operator was applied to mutated binary vectors.…”

Section: Input Layer Optimizationmentioning

confidence: 99%

“…In [226], a modified version of binary PSO was proposed where EA like mutation operator was applied to mutated binary vectors. Similarly, ACO, which traditional solves discrete optimization problem was applied to select input features and training of an FNN in a hybrid manner [228].…”

Section: Input Layer Optimizationmentioning

confidence: 99%

Metaheuristic design of feedforward neural networks: A review of two decades of research

Ojha

Abraham

Snel

2017

Engineering Applications of Artificial Intelligence

457

154

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Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era.

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“…This problem is noisy and multimodal, which makes gradient descent algorithms fail. For these reasons, stochastic search algorithms have been preferred by other researchers: simulated annealing in [15,26], particle swarm optimization method in [27,28], comprehensive learning particle swarm optimization combined with a BFGS algorithm in [29]). The present paper is based on the stochastic search algorithm known as the cross-entropy method (CEM) whose efficiency has been observed in several works.…”

Section: Selection Of the Svr Surrogate Model Parametersmentioning

confidence: 99%

Reliability Assessment With Adaptive Surrogates Based on Support Vector Machine Regression

Bourinet¹

2015

Proceedings of the 1st International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

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Abstract. Reliability assessment in the context of rare failure events still suffers from its computational cost despite some available methods widely accepted by researchers and engineers. Monte Carlo simulation methods even in their most efficient version such as subset simulation often require a large number of samples for an acceptable accuracy on the failure probability of interest. For low to moderately high dimensional problems and under the assumption of a rather smooth limit-state function, surrogate models a.k.a. metamodels or response surfaces represent interesting alternative solutions. This paper presents an adaptive method based on SVM surrogates used in regression. The SVM formulation hinges on the -insensitive loss function and a Gaussian RBF kernel. The key idea of the proposed method is to iteratively construct SVM surrogates which become more accurate as they get closer to the limit-state surface. Subset simulation is applied to the constructed SVM surrogates for assessing probabilities with respect to intermediate threshold values of the LSF and more importantly for generating new training points. The efficiency of the method is tested on several examples featuring various challenges: a parallel system, a highly curved limit-state surface at a single most probable failure point and a smooth high-dimensional limit-state surface with equal curvatures. The paper puts an emphasis on the key role of an optimal selection of SVM surrogate hyperparameters. This is achieved in the present work by minimizing an estimate of the leave-one-out error using the cross-entropy method.652

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Particle swarm optimization for parameter determination and feature selection of support vector machines

Cited by 795 publications

References 25 publications

A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

Metaheuristic design of feedforward neural networks: A review of two decades of research

Reliability Assessment With Adaptive Surrogates Based on Support Vector Machine Regression

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