Parameter determination of support vector machine and feature selection using simulated annealing approach

Lin, Shih-Wei; Lee, Zne-Jung; Chen, Shih‐Chieh; Tseng, Tsung-Yuan

doi:10.1016/j.asoc.2007.10.012

Cited by 299 publications

(116 citation statements)

References 34 publications

Supporting

Mentioning

116

Contrasting

Order By: Relevance

“…This approach, however, involves the need of an expert whose knowledge can be incorporated into the system capable of making accurate forecasts. The most popular algorithms, according to their efficiency, are Support Vector Machines (SVM) [6,7] and Neural Networks (NN) [8,9]. Note that in this paper we will only use SVM since we have not been able to replicate the results from the literature involving NNs.…”

Section: Introductionmentioning

confidence: 75%

Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings

et al. 2013

View full text Add to dashboard Cite

Short-term load forecasting (STLF) in buildings differs from its broader counterpart in that the load to be predicted does not seem to be stationary, seasonal and regular but, on the contrary, it may be subject to sudden changes and variations on its consumption behaviour. Classical STLF methods do not react fast enough to these perturbations (i.e., they are not robust) and the literature on building STLF has not yet explored this area. Hereby, we evaluate a well-known post-processing method (Learning Window Reinitialization) applied to two broadly-used STLF algorithms (Autoregressive Model and Support Vector Machines) in buildings to check their adaptability and robustness. We have tested the proposed method with real-world data and our results state that this methodology is especially suited for buildings with non-regular consumption profiles, as classical STLF methods are enough to model regular-profiled ones.

show abstract

Section: Introductionmentioning

confidence: 75%

Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings

et al. 2013

View full text Add to dashboard Cite

show abstract

“…The basic concept behind SVM is to map the original data sets to higher dimensional features of space and construct an optimal separating plane (SP), from which the distance to all the data points is minimal (Lin et al 2008;Pan et al 2008;Qu and Zuo 2010). Detailed expression of SVM has been extensively reported in numerous studies (Cristianine and Taylor 2000;Raghavendra and Deka, 2014;Vapnik 1998).…”

Section: Support Vector Machinementioning

confidence: 99%

Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China

Shang

Dahlgren

et al. 2017

Environ Sci Pollut Res

101

View full text Add to dashboard Cite

Accurate quantification of dissolved oxygen (DO) is critically important for managing water resources and controlling pollution. Artificial intelligence (AI) models have been successfully applied for modeling DO content in aquatic ecosystems with limited data. However, the efficacy of these AI models in predicting DO levels in the hypoxic river systems having multiple pollution sources and complicated pollutants behaviors is unclear. Given this dilemma, we developed a promising AI model, known as support vector machine (SVM), to predict the DO concentration in a hypoxic river in southeastern China. Four different calibration models, specifically, multiple linear regression, back propagation neural network, general regression neural network, and SVM, were established, and their prediction accuracy was systemically investigated and compared. A total of 11 hydro-chemical variables were used as model inputs. These variables were measured bimonthly at eight sampling sites along the ruralsuburban-urban portion of Wen-Rui Tang River from 2004 to 2008. The performances of the established models were assessed through the mean square error (MSE), determination coefficient (R 2 ), and Nash-Sutcliffe (NS) model efficiency. The results indicated that the SVM model was superior to other models in predicting DO concentration in Wen-Rui Tang River. For SVM, the MSE, R 2 , and NS values for the testing subset were 0.9416 mg/L, 0.8646, and 0.8763, respectively. Sensitivity analysis showed that ammonium-nitrogen was the most significant input variable of the proposal SVM model. Overall, these results demonstrated that the proposed SVM model can efficiently predict water quality, especially for highly impaired and hypoxic river systems.

show abstract

“…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

View full text Add to dashboard Cite

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

show abstract

Parameter determination of support vector machine and feature selection using simulated annealing approach

Cited by 299 publications

References 34 publications

Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings

Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings

Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China

Reliability Assessment With Adaptive Surrogates Based on Support Vector Machine Regression

Contact Info

Product

Resources

About