Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis

B.E. Boser, A training algorithm for optimal margin classifiers, in: ACM Fifth Workshop on Computational Lerning Theory, Pittsburgh, 1992.

Vapnik, 2002, The nature of statistical learning theory, IEEE Trans. Neural Netw., 8

Chen, 2011, A new hybrid method based on local fisher discriminant analysis and support vector machines for hepatitis disease diagnosis, Expert Syst. Appl., 38, 11796, 10.1016/j.eswa.2011.03.066

Chen, 2011, A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis, Expert Syst. Appl., 38, 9014, 10.1016/j.eswa.2011.01.120

Frieb, 1998

Shin, 2005, An application of support vector machines in bankruptcy prediction model, Expert Syst. Appl., 28, 127, 10.1016/j.eswa.2004.08.009

Chen, 2012, Support vector machine based diagnostic system for breast Cancer using swarm intelligence, J. Med. Syst., 36, 2505, 10.1007/s10916-011-9723-0

Maglogiannis, 2009, An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers, Appl. Intell., 30, 24, 10.1007/s10489-007-0073-z

Uuml, 2007, Comparison of different classification algorithms in clinical decision-making, Expert Syst., 24, 17, 10.1111/j.1468-0394.2007.00418.x

Keerthi, 2003, Asymptotic behaviors of support vector machines with Gaussian kernel, Neural Comput., 15, 1667, 10.1162/089976603321891855

Gold, 2002, Model selection for support vector machine classification, Neurocomputing, 55, 221

Chang, 2011, LIBSVM: A library for support vector machines, ACM Trans. Intell. Syst. Technol., 2, 1, 10.1145/1961189.1961199

Chen, 2014, Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy, Appl. Math. Comput., 239, 180

Fu, 2014, 42

Cai, 2017, A new hybrid intelligent framework for predicting parkinson’s disease, IEEE Access, 5, 17188, 10.1109/ACCESS.2017.2741521

Huang, 2006, A GA-based feature selection and parameters optimizationfor support vector machines, Expert Syst. Appl., 31, 231, 10.1016/j.eswa.2005.09.024

Huang, 2019, A new fruit fly optimization algorithm enhanced support vector machine for diagnosis of breast cancer based on high-level features, BMC Bioinformatics, 20, 290, 10.1186/s12859-019-2771-z

Chen, 2019, Efficient multi-population outpost fruit fly-driven optimizers: Framework and advances in support vector machines, Expert Syst. Appl.

Chen, 2011, A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis, Expert Syst. Appl., 38, 9014, 10.1016/j.eswa.2011.01.120

Li, 2017, An enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosis, Comput. Math. Methods Med., 2017, 15, 10.1155/2017/9512741

Mirjalili, 2016, The whale optimization algorithm, Adv. Eng. Softw., 95, 51, 10.1016/j.advengsoft.2016.01.008

Chen, 2019, A balanced whale optimization algorithm for constrained engineering design problems, Appl. Math. Model., 71, 45, 10.1016/j.apm.2019.02.004

Chen, 2019, An efficient double adaptive random spare reinforced whale optimization algorithm, Expert Syst. Appl., 10.1016/j.eswa.2019.113018

Heidari, 2019, An enhanced associative learning-based exploratory whale optimizer for global optimization, Neural Comput. Appl., 10.1007/s00521-019-04015-0

Luo, 2019, Multi-strategy boosted mutative whale-inspired optimization approaches, Appl. Math. Model., 73, 109, 10.1016/j.apm.2019.03.046

Aljarah, 2016, Optimizing connection weights in neural networks using the whale optimization algorithm, Soft Comput., 1

Oliva, 2017, Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm, Appl. Energy, 200, 141, 10.1016/j.apenergy.2017.05.029

Elaziz, 2017, Analysis of bioactive amino acids from fish hydrolysates with a new bioinformatic intelligent system approach, Sci. Rep., 7, 10.1038/s41598-017-10890-1

Harikarthik, 2017, Optimal test suite selection in regression testing with testcase prioritization using modified Ann and Whale optimization algorithm, Cluster Comput., 1

Hassan, 2017, Retinal fundus vasculature multilevel segmentation using whale optimization algorithm, Signal Image Video Process., 1

Kaveh, 2017, Enhanced whale optimization algorithm for sizing optimization of skeletal structures, Mech. Based Des. Struct. Mach., 45, 345, 10.1080/15397734.2016.1213639

Mafarja, 2017, Hybrid whale optimization algorithm with simulated annealing for feature selection, Neurocomputing, 260, 302, 10.1016/j.neucom.2017.04.053

Wu, 2017, Path planning for solar-powered UAV in urban environment, Neurocomputing

Wang, 2017, Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses, Neurocomputing, 267, 69, 10.1016/j.neucom.2017.04.060

B. Scholkopf, C.J.C. Burges, A.J. Smola, Advances in Kernel Methods - Support Vector Learning. 174 (4) (1998) 1097-105.

Rahnamayan, 2007, A novel population initialization method for accelerating evolutionary algorithms, Comput. Math. Appl., 53, 1605, 10.1016/j.camwa.2006.07.013

Coelho, 2008, Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization, Expert Syst. Appl., 34, 1905, 10.1016/j.eswa.2007.02.002

Huang, 2017, Chaos and bifurcation control of torque-stiffness-controlled dynamic bipedal walking, IEEE Trans. Syst. Man Cybern. Syst., 47, 1229, 10.1109/TSMC.2016.2569474

Saremi, 2014, 1077

Chen, 2019, Parameters identification of photovoltaic cells and modules using diversification-enriched harris hawks optimization with chaotic drifts, J. Cleaner Prod.

Zhao, 2019, Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients, Comput. Biol. Chem., 78, 481, 10.1016/j.compbiolchem.2018.11.017

Zhang, 2019, Chaos-induced and mutation-driven schemes boosting salp chains-inspired optimizers, IEEE Access, 7, 31243, 10.1109/ACCESS.2019.2902306

Yu, 2019, Chaos-enhanced synchronized bat optimizer, Appl. Math. Model.

Xu, 2019, An efficient chaotic mutative moth-flame-inspired optimizer for global optimization tasks, Expert Syst. Appl., 129, 135, 10.1016/j.eswa.2019.03.043

Zhang, 2018, Chaos enhanced bacterial foraging optimization for global optimization, IEEE Access, 6, 64905, 10.1109/ACCESS.2018.2876996

Wang, 2014, Chaotic krill herd algorithm, Inform. Sci., 274, 17, 10.1016/j.ins.2014.02.123

Liu, 2015, A fast approach for detection of erythemato-squamous diseases based on extreme learning machine with maximum relevance minimum redundancy feature selection, Internat. J. Systems Sci., 46, 919, 10.1080/00207721.2013.801096

Karthikeyan, 2018, Feature selection and parameters optimization of support vector machines based on hybrid glowworm swarm optimization for classification of diabetic retinopathy, J. Med. Syst., 42, 10.1007/s10916-018-1055-x

Shen, 2016, Evolving support vector machines using fruit fly optimization for medical data classification, Knowl.-Based Syst., 96, 61, 10.1016/j.knosys.2016.01.002

Chen, 2014, Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy, Appl. Math. Comput., 239, 180

Guyon, 2002, Gene selection for cancer classification using support vector machines, Mach. Learn., 46, 389, 10.1023/A:1012487302797

Klöppel, 2008, Automatic classification of MR scans in Alzheimer’s disease, Brain, 131, 681, 10.1093/brain/awm319

El-Dahshan, 2014, Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm, Expert Syst. Appl., 41, 5526, 10.1016/j.eswa.2014.01.021

Hu, 2015, An efficient machine learning approach for diagnosis of paraquat-poisoned patients, Comput. Biol. Med., 59, 116, 10.1016/j.compbiomed.2015.02.003