SVM Parameter Optimization using Grid Search and Genetic Algorithm to Improve Classification Performance

Syarif, Iwan; Prügel-Bennett, Adam; Wills, Gary

doi:10.12928/telkomnika.v14i4.3956

Cited by 373 publications

(189 citation statements)

References 10 publications

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“…In order to detect the changes in online data, a moving average calculation was applied. To support the construction and optimization of the proposed SVR model, a grid search method based on cross-valid training data which is initially introduced in reference [20], is employed for the Energies 2019, 12, 3396 4 of 15 SVR parameters optimization. After that, the temperature detection model of wind turbine gearboxes was setup, followed by the bearing and lubricate temperatures under normal working conditions were predicted.…”

Section: Methodsologymentioning

confidence: 99%

Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data

et al. 2019

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Fault diagnosis and forecasting contribute significantly to the reduction of operating and maintenance associated costs, as well as to improve the resilience of wind turbine systems. Different from the existing fault diagnosis approaches using monitored vibration and acoustic data from the auxiliary equipment, this research presents a novel fault diagnosis and forecasting approach underpinned by a support vector regression model using data obtained by the supervisory control and data acquisition system (SCADA) of wind turbines (WT). To operate, the extraction of fault diagnosis features is conducted by measuring SCADA parameters. After that, confidence intervals are set up to guide the fault diagnosis implemented by the support vector regression (SVR) model. With the employment of confidence intervals as the performance indicators, an SVR-based fault detecting approach is then developed. Based on the WT SCADA data and the SVR model, a fault diagnosis strategy for large-scale doubly-fed wind turbine systems is investigated. A case study including a one-year monitoring SCADA data collected from a wind farm in Southern China is employed to validate the proposed methodology and demonstrate how it works. Results indicate that the proposed strategy can support the troubleshooting of wind turbine systems with high precision and effective response.

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Section: Methodsologymentioning

confidence: 99%

Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data

et al. 2019

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“…The cross validation method divides dataset into v partition (v-fold) randomly and each partition has index number 1 to ν. Common number of partition is 10 partition or 10-fold cross validation (Gaspar et al, 2012;Virmani et al, 2013;Syarif et al, 2016). 10 times tests are conducted for 10 partitions by leave-one-out technique, in which one part is used alternately as test dataset and other parts (v-1) are used as training dataset.…”

Section: Methodsmentioning

confidence: 99%

Determination of SVM-RBF Kernel Space Parameter to Optimize Accuracy Value of Indonesian Batik Images Classification

Tarigan¹,

Agushinta²,

Suhendra³

et al. 2017

Journal of Computer Science

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Image retrieval using Support Vector Machine (SVM) classification very depends on kernel function and parameter. Kernel function used by dot product substitution from old dimension feature to new dimension depends on image dataset condition. In this research, parameter of Gaussian /Radial Basis Function (RBF) kernel function is optimized using multi class non-linear SVM method and implemented to training and test datasets of traditional Indonesian batik images. The batik images dataset is limited to four geometric motifs textures, which are ceplok/ceplokan, kawung, nitik and parang/lerang. Discrete Wavelet Transform level 3 daubechies 2 is used to result feature dataset of traditional batik images dataset of four classes geometric motifs textures. The batik images are used for training and test dataset in SVM-RBF kernel parameter optimation to maximize accuracy value in non-linear multi-class classification. Cross Validation and Grid-search methods are used to analyze and evaluate SVM-RBF kernel parameter optimation. Confusion matrix measurement method is used to result accuracy value in every evaluation conducted in every combination of cost function/C and gamma/γ as SVM-RBF kernel parameter. Maximum accuracy parameter value is C = 2 7 and γ = 2 −15 achieved by 10 times evaluation wit different test dataset for each evaluation. Maximum accuracy value is 0.77 to 0.86.

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“…SVM is a machine learning method for regression and classification [14]. SVM as classifier will make a hyper plane that separates the data into several classes.…”

Section: ) Support Vector Machine (Svm)mentioning

confidence: 99%

“…It can make the performance of machine learning algorithm enhanced and the best result out of all the machine learning method is RF with 67% accuracy. Grid Search also comes with a downside that is, the computational time is slow [14]. To overcome this problem, another optimization method can be used to replace this method.…”

Section: Introductionmentioning

confidence: 99%

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Hyper Parameter Optimization using Genetic Algorithm on Machine Learning Methods for Online News Popularity Prediction

Wicaksono¹,

Supianto²

2018

ijacsa

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Online news is a media for people to get new information. There are a lot of online news media out there and a many people will only read news that is interesting for them. This kind of news tends to be popular and will bring profit to the media owner. That's why, it is necessary to predict whether a news is popular or not by using the prediction methods. Machine learning is one of the popular prediction methods we can use. In order to make a higher accuracy of prediction, the best hyper parameter of machine learning methods need to be determined. Determining the hyper parameter can be time consuming if we use grid search method because grid search is a method which tries all possible combination of hyper parameter. This is a problem because we need a quicker time to make a prediction of online news popularity. Hence, genetic algorithm is proposed as the alternative solution because genetic algorithm can get optimal hypermeter with reasonable time. The result of implementation shows that genetic algorithm can get the hyper parameter with almost the same result with grid search with faster computational time. The reduction in computational time is as follows: Support Vector Machine is 425.06%, Random forest is 17%, Adaptive Boosting is 651.06%, and lastly K -Nearest Neighbour is 396.72%.

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SVM Parameter Optimization using Grid Search and Genetic Algorithm to Improve Classification Performance

Abstract: Abstract

Cited by 373 publications

References 10 publications

Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data

Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data

Determination of SVM-RBF Kernel Space Parameter to Optimize Accuracy Value of Indonesian Batik Images Classification

Hyper Parameter Optimization using Genetic Algorithm on Machine Learning Methods for Online News Popularity Prediction

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