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https://www.sciencedirect.com/science/article/pii/S0957417410011851
Research highlights A genetic algorithm with feature chromosomes (GAFC) is proposed. The asymptotic behaviors of support vector machines (SVM) are fused with GA. The GAFC has not only the search ability of GA, but also has the search ability of feature chromosomes. The GAFC obtained good performances by optimizing feature subset and parameters of SVM …Cited by: 150
https://journals.sagepub.com/doi/full/10.1155/2015/365869
Jul 07, 2015 · This study presents a modified version of cat swarm optimization (MCSO), capable of improving search efficiency within the problem space. The basic CSO algorithm was integrated with a local search procedure as well as the feature selection and parameter optimization of support vector machines (SVMs).Cited by: 15
https://www.researchgate.net/publication/220215684_Feature_selection_and_parameter_optimization_for_support_vector_machines_A_new_approach_based_on_genetic_algorithm_with_feature_chromosomes
Feature selection and parameter optimization for support vector machines: A new approach based on genetic algorithm with feature chromosomes Article in …
https://www.hindawi.com/journals/mpe/2015/604108/
AFSA has proven highly successful in a diversity of applications; however, there remain shortcomings, such as the likelihood of falling into a local optimum and a lack of multiplicity. This study proposes a modified AFSA (MAFSA) to improve feature selection and parameter optimization for support vector machine classifiers.Cited by: 9
http://nlg2.csie.ntu.edu.tw/~cjwang/paper/A%20GA-based%20feature%20selection%20and%20parameters%20optimizationfor%20support%20vector%20machines.pdf
A GA-based feature selection and parameters optimization for support vector machines Cheng-Lung Huang a,*, Chieh-Jen Wang b a Department of Information Management, National Kaohsiung First University of Science and Technology, 2, Juoyue Rd., Nantz District, Kaohsiung 811, Taiwan, ROC b Department of Information Management, Huafan University, 1, Huafan …
https://www.hindawi.com/journals/cin/2017/4135465/
In this paper, a multilayer support vector machine (SVM) based on optimum parameters optimization and feature selection approach has been developed to classify ten types of faults in radial distribution feeders.Cited by: 8
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4175386/
Sep 10, 2014 · SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier. ... Isa D. Feature selection for support vector machine-based face-iris multimodal biometric system. ... Hsu WC, Yu TY. Support vector machines parameter selection based on combined taguchi method and staelin method for e-mail spam filtering.Cited by: 27
https://www.sciencedirect.com/science/article/pii/S0957417405002083
We proposed a GA-based strategy to select the feature subset and set the parameters for SVM classification. As far as we know, previous researches did not perform simultaneous feature selection and parameters optimization for support vector machines.Cited by: 1342
https://dl.acm.org/citation.cfm?id=1930887
Zhi Chen , Tao Lin , Ningjiu Tang , Xin Xia, A Parallel Genetic Algorithm Based Feature Selection and Parameter Optimization for Support Vector Machine, Scientific Programming, 2016, p.1, June 2016 Alaa Tharwat , Aboul Ella Hassanien, Chaotic antlion algorithm for parameter optimization of support vector machine, Applied Intelligence, v.48 n.3 ...Cited by: 150
https://romisatriawahono.net/lecture/rm/paper/Lin%20-%20Parameter%20determination%20and%20feature%20selection%20for%20SVM%20by%20PSO%20-%202009.pdf
Particle swarm optimization for parameter determination and feature selection of support vector machines Shih-Wei Lin a,*, Kuo-Ching Ying b, Shih-Chieh Chen c, Zne-Jung Lee a a Information Management, Huafan University, Taiwan b Industrial Engineering and Management Information, Huafan University, Taiwan c Industrial Management, National Taiwan University …
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