Journal of Engineering and Applied Sciences

Year: 2020
Volume: 15
Issue: 1
Page No. 310 - 318

Selection of Tuning Parameter in L1-Support Vector Machine via. Particle Swarm Optimization Method

Authors : Niam Abdulmunim Al-Thanoon, Omar Saber Qasim and Zakariya Yahya Algamal

References

Algamal, Z.Y. and M.H. Lee, 2015. Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification. Expert Syst. Appl., 42: 9326-9332.
CrossRef  |  Direct Link  |  

Algamal, Z.Y., M.H. Lee, A.M. Al‐Fakih and M. Aziz, 2017. High‐dimensional QSAR classification model for anti‐hepatitis C virus activity of thiourea derivatives based on the sparse logistic regression model with a bridge penalty. J. Chemom., 31: e2889-e2889.
CrossRef  |  Direct Link  |  

Al‐Fakih, A.M., Z.Y. Algamal, M.H. Lee, H.H. Abdallah and H. Maarof et al., 2016. Quantitative structure-activity relationship model for prediction study of corrosion inhibition efficiency using two‐stage sparse multiple linear regression. J. Chemom., 30: 361-368.
CrossRef  |  Direct Link  |  

Becker, N., G. Toedt, P. Lichter and A. Benner, 2011. Elastic SCAD as a novel penalization method for SVM classification tasks in high-dimensional data. BMC. Bioinf., 12: 138-151.
CrossRef  |  PubMed  |  Direct Link  |  

Bi, J., K. Bennett, M. Embrechts, C. Breneman and M. Song, 2003. Dimensionality reduction via sparse support vector machines. J. Mach. Learn. Res., 3: 1229-1243.
CrossRef  |  Direct Link  |  

Bradley, P.S. and O.L. Mangasarian, 1998. Feature selection via concave minimization and support vector machines. Proceedings of the 15th International Conference on Machine Learning (ICML'98) Vol. 98, July 24-27, 1998, ACM, Morgan Kaufmann Publishers Inc., San Francisco, California, USA., ISBN:1-55860-556-8, pp: 82-90.

Broman, K.W. and T.P. Speed, 2002. A model selection approach for the identification of quantitative trait loci in experimental crosses. J. Royal Stat. Soc. Ser. B., 64: 641-656.
CrossRef  |  Direct Link  |  

Cao, G.P., M. Arooj, S. Thangapandian, C. Park and V. Arulalapperumal et al., 2015. A lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors. SAR. QSAR. Environ. Res., 26: 397-420.
CrossRef  |  PubMed  |  Direct Link  |  

Cervantes, J., F. Garcia-Lamont, L. Rodriguez, A. Lopez and J.R. Castilla et al., 2017. PSO-based method for SVM classification on skewed data sets. Neurocomput., 228: 187-197.
CrossRef  |  Direct Link  |  

Chen, J. and Z. Chen, 2008. Extended bayesian information criteria for model selection with large model spaces. Biometrika, 95: 759-771.
CrossRef  |  Direct Link  |  

Chen, K.H., K.J. Wang, K.M. Wang and M.A. Angelia, 2014. Applying particle swarm optimization-based decision tree classifier for cancer classification on gene expression data. Appl. Soft Comput., 24: 773-780.
CrossRef  |  Direct Link  |  

Cong, Y., B.K. Li, X.G. Yang, Y. Xue and Y.Z. Chen et al., 2013. Quantitative structure-activity relationship study of influenza virus neuraminidase A/PR/8/34 (H1N1) inhibitors by genetic algorithm feature selection and support vector regression. Chemom. Intell. Lab. Syst., 127: 35-42.
CrossRef  |  Direct Link  |  

Daszykowski, M., B. Walczak, Q.S. Xu, F. Daeyaert and M.R. de Jonge et al., 2004. Classification and regression trees studies of HIV reverse transcriptase inhibitors. J. Chem. Inf. Comput. Sci., 44: 716-726.
CrossRef  |  PubMed  |  Direct Link  |  

Dong, H. and G. Jian, 2015. Parameter selection of a support vector machine, based on a chaotic particle swarm optimization algorithm. Cybern. Inf. Technol., 15: 140-149.
CrossRef  |  Direct Link  |  

Fan, J. and R. Li, 2001. Variable selection via nonconcave penalized likelihood and its oracle properties. J. Am. Stat. Assoc., 96: 1348-1360.
CrossRef  |  Direct Link  |  

Ikeda, K. and N. Murata, 2005. Geometrical properties of Nu support vector machines with different norms. Neural Comput., 17: 2508-2529.
CrossRef  |  PubMed  |  Direct Link  |  

Jung, Y. and J. Hu, 2015. AK-fold averaging cross-validation procedure. J. Nonparametric Stat., 27: 167-179.
CrossRef  |  PubMed  |  Direct Link  |  

Kennedy, J. and R. Eberhart, 1995. Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Volume 4, November 27-December-1, 1995, Perth, WA., pp: 1942-1948.

Khajeh, A., H. Modarress and H. Zeinoddini‐Meymand, 2012. Application of modified particle swarm optimization as an efficient variable selection strategy in QSAR/QSPR studies. J. Chemom., 26: 598-603.
CrossRef  |  Direct Link  |  

Kiran, M.S., 2017. Particle swarm optimization with a new update mechanism. Appl. Soft Comput., 60: 670-678.
CrossRef  |  Direct Link  |  

Lai, C.M., W.C. Yeh and C.Y. Chang, 2016. Gene selection using information gain and improved simplified swarm optimization. Neurocomputing, 218: 331-338.
CrossRef  |  Direct Link  |  

Li, Y., Y. Kong, M. Zhang, A. Yan and Z. Liu, 2016. Using Support Vector Machine (SVM) for classification of selectivity of H1N1 neuraminidase inhibitors. Mol. Inf., 35: 116-124.
CrossRef  |  PubMed  |  Direct Link  |  

Liang, Y., C. Liu, X.Z. Luan, K.S. Leung and T.M. Chan et al., 2013. Sparse logistic regression with a L1/2 penalty for gene selection in cancer classification. BMC. Bioinf., 14: 1-12.
CrossRef  |  PubMed  |  Direct Link  |  

Lin, S.W., K.C. Ying and S.C. Chen, 2008. Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Syst. Applic., 35: 1817-1824.
CrossRef  |  

Liu, Y., H.H. Zhang, C. Park and J. Ahn, 2007. Support vector machines with adaptive Lq penalty. Comput. Stat. Data Anal., 51: 6380-6394.
CrossRef  |  Direct Link  |  

Liu, Z., S. Lin and M. Tan, 2010. Sparse support vector machines with L_{p} penalty for biomarker identification. IEEE. ACM. Trans. Comput. Biol. Bioinf., 7: 100-107.
CrossRef  |  PubMed  |  Direct Link  |  

Lu, Y., S. Wang, S. Li and C. Zhou, 2011. Particle swarm optimizer for variable weighting in clustering high-dimensional data. Mach. Learn., 82: 43-70.
CrossRef  |  Direct Link  |  

Meijer, R.J. and J.J. Goeman, 2013. Efficient approximate K‐fold and leave‐one‐out cross‐validation for ridge regression. Biom. J., 55: 141-155.
CrossRef  |  PubMed  |  Direct Link  |  

Mirjalili, S. and A. Lewis, 2013. S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm Evolut. Comput., 9: 1-14.
CrossRef  |  Direct Link  |  

Pang, Z., B. Lin and J. Jiang, 2016. Regularisation parameter selection via bootstrapping. Aust. N. Z. J. Stat., 58: 335-356.
CrossRef  |  Direct Link  |  

Park, H., F. Sakaori and S. Konishi, 2014. Robust sparse regression and tuning parameter selection via the efficient bootstrap information criteria. J. Stat. Comput. Simul., 84: 1596-1607.
CrossRef  |  Direct Link  |  

Roberts, S. and G. Nowak, 2014. Stabilizing the lasso against cross-validation variability. Comput. Stat. Data Anal., 70: 198-211.
CrossRef  |  Direct Link  |  

Sabourin, J.A., W. Valdar and A.B. Nobel, 2015. A permutation approach for selecting the penalty parameter in penalized model selection. Biom., 71: 1185-1194.
CrossRef  |  PubMed  |  Direct Link  |  

Shen, Q., W.M. Shi, W. Kong and B.X. Ye, 2007. A combination of modified particle swarm optimization algorithm and support vector machine for gene selection and tumor classification. Talanta, 71: 1679-1683.
CrossRef  |  Direct Link  |  

Wang, L., J. Zhu and H. Zou, 2008. Hybrid huberized support vector machines for microarray classification and gene selection. Bioinf., 24: 412-419.
CrossRef  |  PubMed  |  Direct Link  |  

Wen, J.H., K.J. Zhong, L.J. Tang, J.H. Jiang and H.L. Wu et al., 2011. Adaptive variable-weighted support vector machine as optimized by particle swarm optimization algorithm with application of QSAR studies. Talanta, 84: 13-18.
CrossRef  |  PubMed  |  Direct Link  |  

Xing, J.J., Y.F. Liu, Y.Q. Li, H. Gong and Y.P. Zhou, 2014. QSAR classification model for diverse series of antimicrobial agents using classification tree configured by modified particle swarm optimization. Chemom. Intell. Lab. Syst., 137: 82-90.
CrossRef  |  Direct Link  |  

Zhang, H.H., J. Ahn, X. Lin and C. Park, 2005. Gene selection using support vector machines with non-convex penalty. Bioinf., 22: 88-95.
CrossRef  |  PubMed  |  Direct Link  |  

Zhou, W. and J.A. Dickerson, 2014. A novel class dependent feature selection method for cancer biomarker discovery. Comput. Boil. Med., 47: 66-75.
Direct Link  |  

Zhu, J., S. Rosset, R. Tibshirani and T.J. Hastie, 2004. 1-Norm Support Vector Machines. In: Advances in Neural Information Processing Systems, Thrun, S., L.K. Saul and B. Scholkopf (Eds.). MIT Press, Cambridge, Massachusetts, USA., pp: 49-56.

Design and power by Medwell Web Development Team. © Medwell Publishing 2024 All Rights Reserved