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

Mo, Zeyao; Ren, Jian Wen; Ji, Luping; Fu, Chong; Li, Minglu; Ming-tian, Zhou

doi:10.1007/s00521-011-0603-9

Cited by 28 publications

(13 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here ε-SVR was used with the radial basis function (RBF) as the kernel function. The generalization ability of SVR is affected by 2 parameters, the punishment factor C (C > 0) and the kernel parameter σ 2 [60]. A larger C means the model has less tolerance for errors, leading to overfitting; a smaller C means the model is prone to underfitting.…”

Section: Methodsmentioning

confidence: 99%

A Novel Method for Soil Organic Matter Determination by Using an Artificial Olfactory System

Zhu

Chen

et al. 2019

Sensors

View full text Add to dashboard Cite

Soil organic matter (SOM) is a major indicator of soil fertility and nutrients. In this study, a soil organic matter measuring method based on an artificial olfactory system (AOS) was designed. An array composed of 10 identical gas sensors controlled at different temperatures was used to collect soil gases. From the response curve of each sensor, four features were extracted (maximum value, mean differential coefficient value, response area value, and the transient value at the 20th second). Then, soil organic matter regression prediction models were built based on back-propagation neural network (BPNN), support vector regression (SVR), and partial least squares regression (PLSR). The prediction performance of each model was evaluated using the coefficient of determination (R2), root-mean-square error (RMSE), and the ratio of performance to deviation (RPD). It was found that the R2 values between prediction (from BPNN, SVR, and PLSR) and observation were 0.880, 0.895, and 0.808. RMSEs were 14.916, 14.094, and 18.890, and RPDs were 2.837, 3.003, and 2.240, respectively. SVR had higher prediction ability than BPNN and PLSR and can be used to accurately predict organic matter contents. Thus, our findings offer brand new methods for predicting SOM.

show abstract

Section: Methodsmentioning

confidence: 99%

A Novel Method for Soil Organic Matter Determination by Using an Artificial Olfactory System

Zhu

Chen

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Step 7-Update the current particle speed and location, generate a new generation of population by Equations (18) and (20). Step 8-If the stop condition is met, the iteration is terminated; otherwise, return to step3.…”

Section: Parameter Combinations Of Slssvr Optimized By Psomentioning

confidence: 99%

“…But these methods are inefficient and easy to fall into the local optimal. Zhao et al used the gradient descent method to search the optimal parameters. But the performance of this method is sensitive to the initialization, and it is easy to fall into the local optimal.…”

Section: Introductionmentioning

confidence: 99%

Illumination correction of dyed fabrics method using rotation forest‐based ensemble particle swarm optimization and sparse least squares support vector regression

Zhou

Zhu

et al. 2018

Color Research & Application

View full text Add to dashboard Cite

Different illuminations adversely affect color difference evaluation of textile images in dyed fabrics. To address the problem, we propose a rotation forest (RF)‐based ensemble particle swarm optimization and sparse least squares support vector regression (RF‐PSO‐SLSSVR) for building an accurate illumination correction model. In our algorithm, grey‐edge is first used to extract the statistics characteristics of the textile image. Second, as the standard LSSVR cannot yield a sparse solution, we develop sparse LSSVR (SLSSVR) by calculating the maximal independent subset in the extracted feature space. Then, SLSSVR is embedded into RF by substituting for the regression tree which is the base learner in the original RF, and the PSO technique is employed to obtain the optimal regularization parameter γ and kernel parameter σ. The final model is obtained by fusing the predictions of the different trees through a weighted average method and RF‐PSO‐SLSSVR is constructed to learn the textile illumination estimation model. To verify the effectiveness of our algorithm, we carry out the experiments on the real dyed fabric images by comparison to several related methods and the performance is measured by the different criterions, including the chromaticity error, the angle error, and the Wilcoxon signed‐rank test. Compared with the traditional SVR and ELM algorithm, the results show that the RF‐PSO‐SLSSVR method reduces ~13.6% and 10.6% over the angle RMSE.

show abstract

“…Different from conventional heuristic methods, the GA is a general adaptive optimization searching method based on the Darwinian principle of ''survival of the fittest'' [55][56][57].…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting

Dai

Tang

et al. 2015

Neural Comput & Applic

View full text Add to dashboard Cite

In order to effectively model crude oil spot price with inherently high complexity, a hybrid learning paradigm integrating least squares support vector regression (LSSVR) with a hybrid optimization searching approach for the parameters selection in the LSSVR [consisting of grid method and genetic algorithm (GA)], i.e., a hybrid grid-GA-based LSSVR model, is proposed in this study. In the proposed hybrid learning paradigm, the grid method, a simple but efficient searching method, is first applied to roughly but rapidly determine the proper boundaries of the parameters in the LSSVR; then, the GA, an effective and powerful intelligent searching algorithm, is further implemented to select the most suitable parameters. For illustration and verification, the proposed learning paradigm is used to predict the crude oil spot prices of the West Texas Intermediate and the Brent markets. The empirical results demonstrate that the proposed hybrid grid-GA-based LSSVR learning paradigm can outperform its benchmarking models (including some popular forecasting techniques and similar LSSVRs with other parameter searching algorithms) in terms of both prediction accuracy and time-savings, indicating that it can be utilized as one effective forecasting tool for crude oil price with high volatility and irregularity.

show abstract

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

Cited by 28 publications

References 10 publications

A Novel Method for Soil Organic Matter Determination by Using an Artificial Olfactory System

A Novel Method for Soil Organic Matter Determination by Using an Artificial Olfactory System

Illumination correction of dyed fabrics method using rotation forest‐based ensemble particle swarm optimization and sparse least squares support vector regression

A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting

Contact Info

Product

Resources

About