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A Genetic Algorithm and Support Vector Regression based Hybrid Cost Estimation Model for Feature-based Plastic Injection Products
특징기반 플라스틱 사출제품을 위한 유전자 알고리즘과 Support Vector Regression 기반의 하이브리드 비용 평가 모델
2012
Journal of the Korea Safety Management and Science
특징기반 플라스틱 사출제품을 위한 유전자 알고리즘과 Support Vector Regression 기반의 하이브리드 비용 평가 모델
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A Genetic Algorithm and Support Vector Regression based Hybrid Cost Estimation Model for Feature-based Plastic Injection Products 서 광 규 272 <Figure 1> Procedures of the proposed model for plastic injection ...
A Genetic Algorithm and Support Vector Regression based Hybrid Cost Estimation Model for Feature-based Plastic Injection Products 서 광 규
274 (2) RMSE (root mean squared error) RMSE = ...
doi:10.12812/ksms.2012.14.3.269
fatcat:lnejmpyrz5f3dejxxogynzzaj4
Application of an integrated support vector regression method in prediction of financial returns
2011
International Journal of Information Engineering and Electronic Business
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This study proposes a novel approach, support vector machine method combined with genetic algorithm (GA) for feature selection and chaotic particle swarm optimization(CPSO) for parameter optimization support ...
The advantage of the GA-CPSO-SVR (Support Vector Regression) is that it can deal with feature selection and SVM parameter optimization simultaneously A numerical example is employed to compare the performance ...
Therefore, an integrated Support Vector Regression model is built to overcome the problem. In the new model feature selection and SVM parameter optimization are considered simultaneously. ...
doi:10.5815/ijieeb.2011.03.06
fatcat:oxg4yrgaq5erjcbz6nwp7mf72a
Hybrid genetic algorithms and support vector machines for bankruptcy prediction
2006
Expert systems with applications
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GA is used to optimize both a feature subset and parameters of SVM simultaneously for bankruptcy prediction. ...
This study proposes methods for improving SVM performance in two aspects: feature subset selection and parameter optimization. ...
Acknowledgements This work was supported by the Research Grant from Hallym University, Korea. ...
doi:10.1016/j.eswa.2005.09.070
fatcat:xpwpp4c7wzbeppvlvlumouusdq
GA-optimized Support Vector Regression for an Improved Emotional State Estimation Mod
2014
KSII Transactions on Internet and Information Systems
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Most of these studies have applied multiple regression analysis (MRA), artificial neural network (ANN), and support vector regression (SVR) as the prediction algorithm, but the prediction accuracies have ...
Our proposed algorithm-GASVR-is designed to optimize the kernel parameters and the feature subsets of SVRs in order to predict the levels of two aspects-valence and arousal-of the emotions of the users ...
Our proposed algorithm uses a genetic algorithm (GA) to optimize the parameters and the feature subset selection for SVR. ...
doi:10.3837/tiis.2014.06.014
fatcat:qonx7qhd55bkrdtl2f6yghna7q
GA-Support Vector Regression Based Ship Traffic Flow Prediction
2016
International Journal of Control and Automation
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An integrated Genetic Algorithm (GA) based Support Vector Machine (SVM) model for vessel traffic flow forecasting with input factors selection procession is presented in this paper. ...
GA based SVM forecasting model is established whose parameters were optimized through genetic algorithms. ...
Acknowledgements This work is supported by National Natural Science Foundation of China (51149001). ...
doi:10.14257/ijca.2016.9.2.21
fatcat:pxyiykdij5amvjsvd6fap632ga
Support Vector Machine Parameter Optimization to Improve Liver Disease Estimation with Genetic Algorithm
2020
SinkrOn
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In this study we propose Support Vector Machine optimization parameter with genetic algorithm to get a higher performance of Root Mean Square Error value of SVM in order to estimate the liver disorder. ...
Over the past few decades, machine learning has developed rapidly and it has been introduced for application in medical-related. ...
Support Vector Machine has advantages in overcoming classification (Support Vector Classifier Machine) and regression (Support Vector Regression Machine) tasks both with linear kernel or nonlinear kernel ...
doi:10.33395/sinkron.v4i2.10524
fatcat:cbopvrt5yzbf7cnskqkwxr2w3q
Research on Stock Price Prediction Model based on GA Optimized SVM Parameters
2016
International Journal of Security and Its Applications
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This paper construct the predicted model based on support vector machine (SVM) for the Shanghai Composite Index, acquired the model parameters using genetic algorithm optimization was carried out, combined ...
Therefore, the genetic algorithm optimization vector machine parameter optimization, building support vector machine model to achieve the return on the stock forecast. 278 Copyright ⓒ 2016 SERSC index ...
Acknowledgments The work of this paper is supported by National Natural Science Foundation of China (No.71302153); China Post-Doctoral Program (2014T70838); Shanghai key discipline construction project ...
doi:10.14257/ijsia.2016.10.7.24
fatcat:mape7sihbndn3bi2qans2ay4fi
Parameters Optimization Using Genetic Algorithms in Support Vector Regression for Sales Volume Forecasting
2012
Applied Mathematics
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Support Vector Regression (SVR) has been applied successfully to solve problems in numerous fields and proved to be a better prediction model. ...
Genetic Algorithms (GAs) are used to optimize free parameters of SVR. ...
parameters of support vector regression [19, 20] . ...
doi:10.4236/am.2012.330207
fatcat:o6ze3echavhlfa7ka3oxjnp2di
Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array
2019
Sensors
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Support vector regression (SVR), optimized by the particle swarm optimization (PSO) algorithm, was used to select hyperparameters C and γ to establish the optimal regression model for the purpose of quantitative ...
The fitting effect of SVR optimized by PSO for gas concentration was better than that of SVR and solved the problem of hyperparameters selection. ...
oils Two-dimensional wavelet transformation feature extraction + PLS regression [26] QTY-ANLS for gas mixture Least-squares support vector machine-based (LSSVM-based) nonlinear regression [24] QTY-ANLS ...
doi:10.3390/s19183917
fatcat:gl7a6ldkmfh7xmbvwkhvl5x6de
Feature Selection from Iron Direct Reduction Data Based on Binary Differential Evolution Optimization
2016
Bulletin de la Société royale des sciences de Liège
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The binary differential evolution algorithm is combined to the Least Squares- Support Vector Machine (LS-SVM) regression method to candidate a subset of the effective parameters. ...
In this study, a novel method has been proposed to identify effective parameters in determining the purity of sponge iron in the process of Iron Direct Reduction. ...
Search for optimal subset of features
LS-SVM Regression Support vector machine is an intelligent machine that is learned based on the statistical fundamentals. ...
doi:10.25518/0037-9565.5225
fatcat:pjhk2o3q7fbvtb3w6vap6fjode
Study on Parameter Optimization for Support Vector Regression in Solving the Inverse ECG Problem
2013
Computational and Mathematical Methods in Medicine
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vector regression (SVR) method. ...
Moreover, compared with DE and GA, PSO algorithm is more efficient in parameters optimization and performs better in solving the inverse ECG problem, leading to a more accurate reconstruction of the TMPs ...
Acknowledgments This work is supported by the National Natural Science Foundation of China (30900322, 61070063, and 61272311). ...
doi:10.1155/2013/158056
pmid:23983808
pmcid:PMC3741919
fatcat:glzwpu2545fa7cx7fpsacq76ke
ESTIMATING SOIL MOISTURE USING POLSAR DATA: A MACHINE LEARNING APPROACH
2017
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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For estimating soil moisture, in this study, a model of support vector regression (SVR) is proposed based on obtained data from AIRSAR in 2003 in C, L, and P channels. ...
In this endeavor, sequential forward selection (SFS) and sequential backward selection (SBS) are evaluated to select suitable features of polarized image dataset for high efficient modeling. ...
In the second scenario, optimal input features for support vector model when SVR parameter are not optimized and when SVR parameter are optimized are compared. ...
doi:10.5194/isprs-archives-xlii-4-w4-133-2017
fatcat:zcsiaezkl5dktiwioacyuzs4ny
Modeling Energy Gap of Doped Tin (II) Sulfide Metal Semiconductor Nanocatalyst Using Genetic Algorithm-Based Support Vector Regression
2022
Journal of Nanomaterials
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This work employs lattice parameter descriptors to develop a hybrid genetic algorithm (GA) and support vector regression algorithm (SVR) intelligent model for determining the energy gap of doped SnS semiconductors ...
Tin (II) sulfide (SnS) is a metal chalcogenide semiconducting material with fascinating and admirable physical features for practical applications in solid-state batteries, photodetectors, gas sensors, ...
Acknowledgments The support received from Tertiary Education Trust Fund through Adekunle Ajasin University, Akungba Akoko, is well appreciated. ...
doi:10.1155/2022/8211023
fatcat:x6ptvsfdozbzrear53zkdcupce
A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction
2013
Mathematical and computer modelling
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This study presents a hybrid approach, known as real-value genetic algorithm support vector regression (RGA-SVR), which searches for the optimal SVR parameters using real-value genetic algorithms, and ...
A prediction model based on support vector regression (SVR) is proposed in this paper to solve the aquaculture water quality prediction problem. ...
This study presents a hybrid approach, known as real-value genetic algorithm support vector regression (RGA-SVR), which searches for the optimal SVR parameters using real-value genetic algorithms, and ...
doi:10.1016/j.mcm.2011.11.021
fatcat:eq4wfr2mqvho7jsggokiw4dqaq
Hybrid Support Vector Regression and Genetic Algorithm Technique - A Novel Approach in Process Modeling
2009
Chemical product and process modeling
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The method incorporates hybrid support vector regression and genetic algorithm technique (SVR-GA) for efficient tuning of SVR meta parameters. ...
This paper describes a robust support vector regression (SVR) methodology, which can offer superior performance for important process engineering problems. ...
Table 2 shows the typical data selected for support vector regression. ...
doi:10.2202/1934-2659.1329
fatcat:xbcpvffgqvhvxkszyyici5zlky
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