Biomedical classification application and parameters optimization of mixed kernel SVM based on the information entropy particle swarm optimization

References

• Vapnik V. The nature of statistical learning theory. Berlin, Heidelberg: Springer Science & Business Media; 2013.
   Google Scholar
• Khodor N, Matelot D, Carrault G, et al. Kernel based support vector machine for the early detection of syncope during head-up tilt test. Physiol Meas. 2014; 35:2119–2134.
   PubMed Web of Science ®Google Scholar
• Yu C, Deng M, Zheng L, et al. DFA7, a new method to distinguish between intron-containing and intronless genes. PLoS One. 2014;9:e101363.
   PubMed Web of Science ®Google Scholar
• Mao R, Kumar PKR, Guo C, et al. Comparative analyses between retained introns and constitutively spliced introns in Arabidopsis thaliana using random forest and support vector machine. PLoS One. 2014;9:e104049.
   PubMed Web of Science ®Google Scholar
• Chen Y, Sun J, Huang LC, et al. Classification of cancer primary sites using machine learning and somatic mutations. Biomed Res Int. 2015;2015:1–9.
   Web of Science ®Google Scholar
• Spilka J, Frecon J, Leonarduzzi R, et al. Sparse support vector machine for intrapartum fetal heart rate classification. IEEE J Biomed Health Inform. 2016;99:1.
   Google Scholar
• Kung SY. Kernel methods and machine learning. Cambridge: Cambridge University Press; 2014.
   Google Scholar
• Dioşan L, Rogozan A, Pecuchet JP. Improving classification performance of support vector machine by genetically optimising kernel shape and hyper-parameters. Appl Intell. 2012;36:280–294.
   Web of Science ®Google Scholar
• Yuan L, Chen F, Zhou L, et al. Improve scene classification by using feature and kernel combination. Neurocomputing. 2015;170:213–220.
   Web of Science ®Google Scholar
• Binol H, Bal A, Cukur H. Differential evolution algorithm-based kernel parameter selection for Fukunaga–Koontz transform subspaces construction. Proceedings of the SPIE, High-Performance Computing in Remote Sensing V; October 2015. p. 9646.
   Google Scholar
• Keerthi SS, Lin CJ. Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Comput. 2003;15:1667–1689.
   PubMed Web of Science ®Google Scholar
• Lin HT, Lin CJ. A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods. Neural Comput. 2003;1–32.
   PubMed Web of Science ®Google Scholar
• Ricatte T, Garriga G, Gilleron R, et al. Learning from multiple graphs using a sigmoid kernel//machine learning and applications (ICMLA). IEEE on the 12th International Conference, Vol. 2; 2013. p. 140–145.
   Google Scholar
• Remaki L, Cheriet M. KCS-new kernel family with compact support in scale space: formulation and impact. IEEE Trans Image Process. 2000;9:970–981.
   PubMed Web of Science ®Google Scholar
• Scholkopf B, Smola AJ. Learning with kernels: support vector machines, regularization, optimization, and beyond. Cambridge: MIT Press; 2001.
   Google Scholar
• Smits GF, Jordaan EM. Improved SVM regression using mixtures of kernels. IJCNN'02. Proceedings of the 2002 International Joint Conference on IEEE, Vol. 3. 2002. p. 2785–2790.
   Google Scholar
• Lanckriet GRG, Cristianini N, Bartlett P, et al. Learning the kernel matrix with semidefinite programming. J Mach Learn Res. 2004;5:27–72.
   Web of Science ®Google Scholar
• Jagarlapudi SN, Dinesh G, Raman S, et al. On the algorithmics and applications of a mixed-norm based kernel learning formulation. Adv Neural Inf Process Syst. 2009;844–852.
   Google Scholar
• Cortes C, Mohri M, Rostamizadeh A. Learning non-linear combinations of kernels. Adv Neural Inf Process Syst. 2009;22:396–404.
   Google Scholar
• Kumar A, Ghosh SK, Dadhwal VK. Study of mixed kernel effect on classification accuracy using density estimation. Proceedings of the ISPRS Commission VII Symposium 36 (Part 7); 2006.
   Google Scholar
• Hovestadt T, Binzenhöfer B, Nowicki P, et al. Do all inter‐patch movements represent dispersal? A mixed kernel study of butterfly mobility in fragmented landscapes. J Anim Ecol. 2011;80:1070–1077.
   PubMed Web of Science ®Google Scholar
• Marcou GG, Horvath D, Varnek A. Kernel target alignment parameter – a new model ability measure for regression tasks. J Chem Inf Model. 2016;56:6–11.
   PubMed Web of Science ®Google Scholar
• Chapelle O, Vapnik V, Bousquet O, et al. Choosing multiple parameters for support vector machines. Mach Learn. 2002;46:131–159.
   Web of Science ®Google Scholar
• Fernandez M, Caballero J, Fernandez L, et al. Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM). Mol Divers. 2011;15:269–289.
   PubMed Web of Science ®Google Scholar
• Subasi A. Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Comput Biol Med. 2013;43:576–586.
   PubMed Web of Science ®Google Scholar
• Ardjani F, Sadouni K, Benyettou M. Optimization of SVM multiclass by particle swarm (PSO-SVM)//Database Technology and Applications (DBTA). 2010 2nd International Workshop on IEEE; 2010. p. 1–4.
   Google Scholar
• Huang CL, Dun JF. A distributed PSO–SVM hybrid system with feature selection and parameter optimization. Appl Soft Comput. 2008;8:1381–1391.
   Web of Science ®Google Scholar
• Mercer J. Functions of positive and negative type, and their connection with the theory of integral equations. Philos Trans R Soc Lond. Ser A: Containing Papers of a Mathematical or Physical Character. 1909;209:415–446.
   Google Scholar
• Kennedy J. Particle swarm optimization. In: Encyclopedia of machine learning. US: Springer. 2010. p. 760–766.
   Google Scholar
• Tang Y, Wang Z, Fang J. Feedback learning particle swarm optimization. Appl Soft Comput. 2011;11:4713–4725.
   Web of Science ®Google Scholar
• Shannon CE. A mathematical theory of communication. ACM SIGMOBILE Mobile Comput Commun Rev. 2001;5:3–55.
   Google Scholar
• Hu Q, Pan W, An S, et al. An efficient gene selection technique for cancer recognition based on neighborhood mutual information. Int J Mach Learn Cybern. 2010;1:63–74.
   Web of Science ®Google Scholar
• Machine Learning Repository; 2016. Available from: http://archive.ics.uci.edu/ml/.
   Google Scholar
• Cancer Program Datasets; 2016. Available from: http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi.
   Google Scholar
• Little MA, McSharry PE, Roberts SJ, et al. Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. BioMed Eng Online. 2007;6:23.
   PubMed Web of Science ®Google Scholar
• Kononenko I, Simec E, Robnik-Sikonja M. Overcoming the myopia of inductive learning algorithms with RELIEFF. Appl Intell. 1997;7:39–55.
   Web of Science ®Google Scholar
• Hoshida Y, Brunet JP, Tamayo P, et al. Subclass mapping: identifying common subtypes in independent disease data sets. PLoS One. 2007;2:e1195.
   PubMed Web of Science ®Google Scholar
• Michalski RS, Mozetic I, Hong J, et al. The multi-purpose incremental learning system AQ15 and its testing application to three medical domains. Proceedings of AAAI; 1986. p. 1041–1045.
   Google Scholar
• Monti S, Tamayo P, Mesirov J, et al. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach Learn. 2003;52:91–118.
   Web of Science ®Google Scholar