A GA-based feature selection and parameters optimizationfor support vector machines

Huang, Cheng-Lung; Wang, Chieh-Jen

doi:10.1016/j.eswa.2005.09.024

Cited by 1,242 publications

(602 citation statements)

References 11 publications

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“…In addition to the commonly used GS, other techniques were also employed in SVR (or SVM) to correct appropriate values of hyper-parameters. Huang and Wang (2006) presented a GA-based feature selection and parameters' optimization for SVM. Also, Momma and Bennett (2002) developed a fully automated pattern search (PS) methodology for model selection of SVR.…”

Section: Development Of Modelmentioning

confidence: 99%

Improvement of the prediction performance of a soft sensor model based on support vector regression for production of ultra-low sulfur diesel

Shokri

Sadeghi

Marvast³

et al. 2015

Pet. Sci.

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A novel data-driven, soft sensor based on support vector regression (SVR) integrated with a data compression technique was developed to predict the product quality for the hydrodesulfurization (HDS) process. A wide range of experimental data was taken from a HDS setup to train and test the SVR model. Hyper-parameter tuning is one of the main challenges to improve predictive accuracy of the SVR model. Therefore, a hybrid approach using a combination of genetic algorithm (GA) and sequential quadratic programming (SQP) methods (GA-SQP) was developed. Performance of different optimization algorithms including GA-SQP, GA, pattern search (PS), and grid search (GS) indicated that the best average absolute relative error (AARE), squared correlation coefficient (R 2 ), and computation time (CT) (AARE = 0.0745, R 2 = 0.997 and CT = 56 s) was accomplished by the hybrid algorithm. Moreover, to reduce the CT and improve the accuracy of the SVR model, the vector quantization (VQ) technique was used. The results also showed that the VQ technique can decrease the training time and improve prediction performance of the SVR model. The proposed method can provide a robust, soft sensor in a wide range of sulfur contents with good accuracy.

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Section: Development Of Modelmentioning

confidence: 99%

Improvement of the prediction performance of a soft sensor model based on support vector regression for production of ultra-low sulfur diesel

Shokri

Sadeghi

Marvast³

et al. 2015

Pet. Sci.

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show abstract

“…Two methods were considered in this study: genetic algorithms (GA) and sequential forward selection (SFS). A GA approach (Huang and Wang, 2006) for feature selection is here used as an alternative to the conventional heuristic method SFS (Theodoridis and Koutroumbas, 2008). In the case of SFS method, the classification accuracy is used as selection criteria, thus, an exhaustive analysis was conducted.…”

Section: Feature Selectionmentioning

confidence: 99%

“…Eng. 2017 September;33(3): 202-217 scatter and maximizes the between-cluster separation, such as proposed by Huang and Wang (2006). Both methods are carried out following two approaches: in the first one, features from the four channels are considered as a whole dataset using all-channel analysis (GA and SFS).…”

Section: Feature Selectionmentioning

confidence: 99%

Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees

et al. 2017

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Introduction: Intuitive prosthesis control is one of the most important challenges in order to reduce the user effort in learning how to use an artificial hand. This work presents the development of a novel method for pattern recognition of sEMG signals able to discriminate, in a very accurate way, dexterous hand and fingers movements using a reduced number of electrodes, which implies more confidence and usability for amputees. Methods: The system was evaluated for ten forearm amputees and the results were compared with the performance of able-bodied subjects. Multiple sEMG features based on fractal analysis (detrended fluctuation analysis and Higuchi's fractal dimension) combined with traditional magnitude-based features were analyzed. Genetic algorithms and sequential forward selection were used to select the best set of features. Support vector machine (SVM), K-nearest neighbors (KNN) and linear discriminant analysis (LDA) were analyzed to classify individual finger flexion, hand gestures and different grasps using four electrodes, performing contractions in a natural way to accomplish these tasks. Statistical significance was computed for all the methods using different set of features, for both groups of subjects (able-bodied and amputees). Results: The results showed average accuracy up to 99.2% for able-bodied subjects and 98.94% for amputees using SVM, followed very closely by KNN. However, KNN also produces a good performance, as it has a lower computational complexity, which implies an advantage for real-time applications. Conclusion:The results show that the method proposed is promising for accurately controlling dexterous prosthetic hands, providing more functionality and better acceptance for amputees.

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“…1. The algorithm starts by generating a random population of individuals, binary encoded with dimension equal to the initial number of features, each chromosome representing if a feature is used in the subset represented by the individual [19]. The individuals are evaluated using the ANN modeling method and depending on the performance of the individuals, they are selected for the creation of the next generation.…”

Section: Genetic Algorithmmentioning

confidence: 99%

GA-ANN Short-Term Electricity Load Forecasting

Viegas

Vieira

Melício

et al. 2016

Technological Innovation for Cyber-Physical Systems

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A GA-based feature selection and parameters optimizationfor support vector machines

Cited by 1,242 publications

References 11 publications

Improvement of the prediction performance of a soft sensor model based on support vector regression for production of ultra-low sulfur diesel

Improvement of the prediction performance of a soft sensor model based on support vector regression for production of ultra-low sulfur diesel

Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees

GA-ANN Short-Term Electricity Load Forecasting

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