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Evolutionary Machine Learning Techniques pp 11–34Cite as

Salp Chain-Based Optimization of Support Vector Machines and Feature Weighting for Medical Diagnostic Information Systems

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Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Nowadays, medical diagnosis based on machine learning is an essential, active, and interdisciplinary research area. Making smart diagnosis and decision support systems have a profound impact on healthcare informatics. Integrating machine learning classifier systems into computer-aided diagnosis systems promotes the early detection of diseases, which results in more effective treatments and prolonged survival. In this chapter, we address popular diagnosis problems using an evolutionary machine learning approach which performs feature weighting and tuning the parameters of support vector machines (SVMs) simultaneously. A new and powerful metaheuristic called salp swarm algorithm is combined with SVM for this task. The designed SSA-SVM approach shows several merits compared to other SVM-based frameworks with well-regarded algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO).

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Notes

  1. 1.

    The content of this section is based on the lecture note on Mathematical Foundations of Artificial Intelligence of Prof. Daniel Gildea at University of Rochester [77]. We acknowledge their efforts for providing such useful materials, publicly. For full access, refer to http://www.cs.rochester.edu/~gildea/.

References

  1. Cios KJ, Pedrycz W, Swiniarski RW (2012) Data mining methods for knowledge discovery, vol 458. Springer Science & Business Media

    Google Scholar 

  2. Friedman LF (2014) Ibm’s watson supercomputer may soon be the best doctor in the world. Bus Insid, Sci

    Google Scholar 

  3. Ambrosio L, Portillo C, Rodríguez-Blázquez C, Rodriguez-Violante M, Castrillo JCM, Arillo VC, Garretto NS, Arakaki T, Dueñas MS, Álvarez M et al (2016) Living with chronic illness scale: international validation of a new self-report measure in parkinson’s disease. npj Parkinson’s Dis 2:16022

    Google Scholar 

  4. Statistics: release calendar, Mar 2019

    Google Scholar 

  5. Kalmady SV, Greiner R, Agrawal R, Shivakumar V, Narayanaswamy JC, Brown MRG, Greenshaw AJ, Dursun SM, Venkatasubramanian G (2019) Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning. Npj Schizophr 5(1):2

    Article  Google Scholar 

  6. Spasov S, Passamonti L, Duggento A, Lio P, Toschi N, Neuroimaging Initiative Alzheimer’s Disease et al (2019) A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to alzheimer’s disease. NeuroImage 189:276–287

    Article  Google Scholar 

  7. Liu Z, Yao C, Hang Y, Taihua W (2019) Deep reinforcement learning with its application for lung cancer detection in medical internet of things. Futur Gener Comput Syst

    Google Scholar 

  8. Mostafa SA, Mustapha A, Mohammed MA, Hamed RI, Arunkumar N, Ghani MKA, Jaber MM, Khaleefah SH (2019) Examining multiple feature evaluation and classification methods for improving the diagnosis of parkinson’s disease. Cogn Syst Res 54:90–99

    Article  Google Scholar 

  9. Ting FF, Tan YJ, Sim KS (2019) Convolutional neural network improvement for breast cancer classification. Expert Syst Appl 120:103–115

    Article  Google Scholar 

  10. Brunetti A, Carnimeo L, Trotta GF, Bevilacqua V (2019) Computer-assisted frameworks for classification of liver, breast and blood neoplasias via neural networks: a survey based on medical images. Neurocomputing 335:274–298

    Article  Google Scholar 

  11. Choudhury A, Gupta D (2019) A survey on medical diagnosis of diabetes using machine learning techniques. In: Recent developments in machine learning and data analytics. Springer, pp 67–78

    Google Scholar 

  12. Das H, Naik B, Behera HS (2018) Classification of diabetes mellitus disease (dmd): a data mining (dm) approach. In: Progress in computing, analytics and networking, pp 539–549. Springer

    Google Scholar 

  13. Ndaba M, Pillay AW, Ezugwu AE (2018) An improved generalized regression neural network for type ii diabetes classification. In: International conference on computational Science and its applications. Springer, pp 659–671

    Google Scholar 

  14. Haque MR, Islam MM, Iqbal H, Reza MS, Hasan MK (2018) Performance evaluation of random forests and artificial neural networks for the classification of liver disorder. In: 2018 international conference on computer, communication, chemical, material and electronic engineering (IC4ME2), pp 1–5. IEEE

    Google Scholar 

  15. Kumar S, Katyal S (2018) Effective analysis and diagnosis of liver disorder by data mining. In: 2018 international conference on inventive research in computing applications (ICIRCA), pp 1047–1051. IEEE

    Google Scholar 

  16. AlAgha AS, Faris H, Hammo BH, A-Zoubi AM (2018) Identifying \(\beta \)-thalassemia carriers using a data mining approach: The case of the gaza strip, palestine. Artif Intell Med 88:70–83

    Article  Google Scholar 

  17. Das V, Dandapat S, Bora PK (2019) A novel diagnostic information based framework for super-resolution of retinal fundus images. Comput Med Imaging Graph

    Google Scholar 

  18. Goyal H, Khandelwal D, Aggarwal A, Bhardwaj P (2018) Medical diagnosis using machine learning. Bhagwan Parshuram Inst Technol 7

    Google Scholar 

  19. Samant P, Agarwal R (2018) Machine learning techniques for medical diagnosis of diabetes using iris images. Comput Methods Programs Biomed 157:121–128

    Article  Google Scholar 

  20. Saqlain SM, Sher M, Shah FA, Khan I, Ashraf MU, Awais M, Ghani A (2018) Fisher score and matthews correlation coefficient-based feature subset selection for heart disease diagnosis using support vector machines. Knowl Inf Syst 1–29

    Google Scholar 

  21. Sidey-Gibbons JAM, Sidey-Gibbons JAM (2019) Machine learning in medicine: a practical introduction. BMC Med Res Methodol 19(1):64

    Google Scholar 

  22. Zheng X, Lv G, Zhang Y, Lv X, Gao Z, Tang J, Mo J (2019) Rapid and non-invasive screening of high renin hypertension using raman spectroscopy and different classification algorithms. Spectrochim Acta Part A: Mol Biomol Spectrosc 215:244–248

    Article  Google Scholar 

  23. Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10(5):988–999

    Article  Google Scholar 

  24. Byvatov E, Schneider G (2003) Support vector machine applications in bioinformatics. Appl Bioinform 2(2):67–77

    Google Scholar 

  25. Staples M, Chan L, Si D, Johnson K, Whyte C, Cao R (2019) Artificial intelligence for bioinformatics: Applications in protein folding prediction. bioRxiv, pp 561027

    Google Scholar 

  26. Burges Christopher JC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121–167

    Article  Google Scholar 

  27. Wu H, Qing H, Daqing W, Lifu G (2018) A cnn-svm combined model for pattern recognition of knee motion using mechanomyography signals. J Electromyogr Kinesiol 42:136–142

    Article  Google Scholar 

  28. Al-Zoubi Ala M, Faris Hossam, Hassonah Mohammad A et al (2018) Evolving support vector machines using whale optimization algorithm for spam profiles detection on online social networks in different lingual contexts. Knowl-Based Syst 153:91–104

    Article  Google Scholar 

  29. Aljarah I, Al-Zoubi AM, Faris H, Hassonah MA, Mirjalili S, Saadeh H (2018) Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm. Cogn Comput 1–18

    Google Scholar 

  30. Sadiq AS, Faris H, Al-Zoubi AM, Mirjalili S, Ghafoor KZ (2019) Fraud detection model based on multi-verse features extraction approach for smart city applications. In: Smart cities cybersecurity and privacy. Elsevier, pp 241–251

    Google Scholar 

  31. Naik VA, Desai AA (2018) Online handwritten gujarati numeral recognition using support vector machine

    Google Scholar 

  32. Niu X-X, Suen CY (2012) A novel hybrid cnn-svm classifier for recognizing handwritten digits. Pattern Recognit 45(4):1318–1325

    Article  Google Scholar 

  33. Xuelian D, Yuqing L, Jian W, Jilian Z (2019) Feature selection for text classification: a review. Multimed Tools Appl 78(3):3797–3816

    Article  Google Scholar 

  34. Mohammad AH, Alwada’n T, Al-Momani O (2018) Arabic text categorization using support vector machine, naïve bayes and neural network. GSTF J Comput (JoC) 5(1):

    Google Scholar 

  35. Chandra MA, Bedi SS (2018) Survey on svm and their application in image classification. Int J Inf Technol 1–11

    Google Scholar 

  36. Sudharshan PJ, Petitjean C, Spanhol F, Oliveira LE, Heutte L, Honeine P (2019) Multiple instance learning for histopathological breast cancer image classification. Expert Syst Appl 117:103–111

    Article  Google Scholar 

  37. Faris H, Hassonah MA, Al-Zoubi AM, Mirjalili S, Aljarah I (2018) A multi-verse optimizer approach for feature selection and optimizing svm parameters based on a robust system architecture. Neural Comput Appl 30(8):2355–2369

    Article  Google Scholar 

  38. Phan AV, Nguyen ML, Bui LT (2017) Feature weighting and svm parameters optimization based on genetic algorithms for classification problems. Appl Intell 46(2):455–469

    Article  Google Scholar 

  39. Lameski P, Zdravevski E, Mingov R, Kulakov A (2015) Svm parameter tuning with grid search and its impact on reduction of model over-fitting. In: Rough sets, fuzzy sets, data mining, and granular computing. Springer, pp 464–474

    Google Scholar 

  40. Staelin C (2003) Parameter selection for support vector machines. Hewlett-Packard Company, Tech. Rep. HPL-2002-354R1

    Google Scholar 

  41. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Article  Google Scholar 

  42. Lichman M (2013) UCI machine learning repository

    Google Scholar 

  43. Wettschereck D, Aha DW, Mohri T (1997) A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artif Intell Rev 11(1–5):273–314

    Article  Google Scholar 

  44. Heidari AA, Pahlavani P (2017) An efficient modified grey wolf optimizer with lévy flight for optimization tasks. Appl Soft Comput 60:115–134

    Article  Google Scholar 

  45. Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073

    Article  Google Scholar 

  46. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  47. Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849–872

    Article  Google Scholar 

  48. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249

    Article  Google Scholar 

  49. Hadi E, Ali S, Ardeshir B, Mohd H (2012) Water cycle algorithm-a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput & Struct 110:151–166

    Google Scholar 

  50. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513

    Article  Google Scholar 

  51. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98

    Article  Google Scholar 

  52. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  53. Heidari AA, Abbaspour RA, Jordehi AR (2017) An efficient chaotic water cycle algorithm for optimization tasks. Neural Comput Appl 28(1):57–85

    Article  Google Scholar 

  54. Yang X-S (2010) Nature-inspired metaheuristic algorithms. Luniver press,

    Google Scholar 

  55. Guo XC, Yang JH, Wu CG, Wang CY, Liang YC (2008) A novel ls-svms hyper-parameter selection based on particle swarm optimization. Neurocomputing 71(16–18):3211–3215

    Article  Google Scholar 

  56. Chunhong Z, Licheng J (2004) Automatic parameters selection for svm based on ga. In: Fifth world congress on intelligent control and automation (IEEE Cat. No. 04EX788), vol 2, pp 1869–1872. IEEE

    Google Scholar 

  57. Nanda MA, Seminar KB, Solahudin M, Maddu A, Nandika D (2018) Implementation of genetic algorithm (ga) for hyperparameter optimization in a termite detection system. In: Proceedings of the 2nd international conference on graphics and signal processing. ACM, pp 100–104

    Google Scholar 

  58. Ren Y, Bai G (2010) Determination of optimal svm parameters by using ga/pso. JCP 5(8):1160–1168

    Google Scholar 

  59. Jin Q, Chi M, Zhang Y, Wang H, Zhang H, Cai W (2018) A novel bacterial algorithm for parameter optimization of support vector machine. In: 2018 37th Chinese control conference (CCC), pp 3252–3257. IEEE

    Google Scholar 

  60. Sayed GI, Soliman M, Hassanien AE (2019) Parameters optimization of support vector machine based on the optimal foraging theory. In: Machine learning paradigms: theory and application. Springer, pp 309–326

    Google Scholar 

  61. Mirjalili S (2016) Sca: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133

    Article  Google Scholar 

  62. Ilhan A, Mehmet K, Erhan A (2011) A multi-objective artificial immune algorithm for parameter optimization in support vector machine. Appl Soft Comput 11(1):120–129

    Article  Google Scholar 

  63. Godínez-Bautista A, Padierna LC, Rojas-Domínguez A, Puga H, Carpio M (2018) Bio-inspired metaheuristics for hyper-parameter tuning of support vector machine classifiers. In: Fuzzy logic augmentation of neural and optimization algorithms: theoretical aspects and real applications. Springer, pp 115–130

    Google Scholar 

  64. Mafarja M, Aljarah I, Faris H, Hammouri AI, Al-Zoubi AM, Mirjalili S (2019) Binary grasshopper optimisation algorithm approaches for feature selection problems. Expert Syst Appl 117:267–286

    Article  Google Scholar 

  65. Mafarja M, Aljarah I, Heidari AA, Faris H, Fournier-Viger P, Li X, Mirjalili S (2018) Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowl-Based Syst 161:185–204

    Article  Google Scholar 

  66. Mafarja M, Aljarah I, Heidari AA, Hammouri AI, Faris H, Al-Zoubi AM, Mirjalili S (2018) Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems. Knowl-Based Syst 145:25–45

    Article  Google Scholar 

  67. Mafarja M, Heidari AA, Faris H, Mirjalili S, Aljarah I (2020) Dragonfly algorithm: theory, literature review, and application in feature selection. In: Nature-Inspired Optimizers. Springer, pp 47–67

    Google Scholar 

  68. Chaoshun Li, Xueli An, Ruhai Li (2015) A chaos embedded gsa-svm hybrid system for classification. Neural Computing and Applications 26(3):713–721

    Article  Google Scholar 

  69. Huang C-L, Dun J-F (2008) A distributed pso-svm hybrid system with feature selection and parameter optimization. Appl Soft Comput 8(4):1381–1391

    Article  Google Scholar 

  70. Liu Y, Wang G, Chen H, Dong H, Zhu X, Wang S (2011) An improved particle swarm optimization for feature selection. J Bionic Eng 8(2):191–200

    Article  Google Scholar 

  71. Bouraoui A, Jamoussi S, BenAyed Y (2017) A multi-objective genetic algorithm for simultaneous model and feature selection for support vector machines. Artif Intell Rev 1–21

    Google Scholar 

  72. Huang C-L, Wang C-J (2006) A ga-based feature selection and parameters optimizationfor support vector machines. Expert Syst Appl 31(2):231–240

    Article  Google Scholar 

  73. Sarafrazi S, Nezamabadi-pour H (2013) Facing the classification of binary problems with a gsa-svm hybrid system. Math Comput Model 57(1–2):270–278

    Article  MathSciNet  MATH  Google Scholar 

  74. Aladeemy M, Tutun S, Khasawneh MT (2017) A new hybrid approach for feature selection and support vector machine model selection based on self-adaptive cohort intelligence. Expert Syst Appl 88:118–131

    Article  Google Scholar 

  75. Costa VO, Rodrigues CR (2018) Hierarchical ant colony for simultaneous classifier selection and hyperparameter optimization. In: 2018 IEEE congress on evolutionary computation (CEC), pp 1–8. IEEE

    Google Scholar 

  76. Huang C-L (2009) Aco-based hybrid classification system with feature subset selection and model parameters optimization. Neurocomputing 73(1–3):438–448

    Article  Google Scholar 

  77. Gildea D, Naim I (2013) CSC 446 notes: Lecture 7

    Google Scholar 

  78. Faris H, Mirjalili S, Aljarah I, Mafarja M, Heidari AA (2020) Salp swarm algorithm: theory, literature review, and application in extreme learning machines. Springer International Publishing, Cham, pp 185–199

    Google Scholar 

  79. Aljarah I, Mafarja M, Heidari AA, Faris H, Zhang Y, Mirjalili S (2018) Asynchronous accelerating multi-leader salp chains for feature selection. Appl Soft Comput 71:964–979

    Article  Google Scholar 

  80. Faris H, Mafarja MM, Heidari AA, Aljarah I, Al-Zoubi AM, Mirjalili S, Fujita H (2018) An efficient binary salp swarm algorithm with crossover scheme for feature selection problems. Knowl-Based Syst 154:43–67

    Article  Google Scholar 

  81. Zhang Q, Chen H, Heidari AA, Zhao X, Xu Y, Wang P, Li Y, Li C (2019) Chaos-induced and mutation-driven schemes boosting salp chains-inspired optimizers. IEEE Access 7:31243–31261

    Article  Google Scholar 

  82. Lichman M et al (2013) Uci machine learning repository

    Google Scholar 

  83. Faris H, Al-Zoubi AM, Heidari AA, Aljarah I, Mafarja M, Hassonah MA, Fujita H (2019) An intelligent system for spam detection and identification of the most relevant features based on evolutionary random weight networks. Inf Fusion 48:67–83

    Article  Google Scholar 

  84. Wolpert DH, Macready WG et al (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan

    Ala’ M. Al-Zoubi, Maria Habib, Hossam Faris, Ibrahim Aljarah & Mohammad A. Hassonah

  2. School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Ali Asghar Heidari

  3. Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore

    Ali Asghar Heidari

Authors
  1. Ala’ M. Al-Zoubi
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  2. Ali Asghar Heidari
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  3. Maria Habib
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  4. Hossam Faris
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  5. Ibrahim Aljarah
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  6. Mohammad A. Hassonah
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Corresponding author

Correspondence to Hossam Faris .

Editor information

Editors and Affiliations

  1. Torrens University Australia, Brisbane, QLD, Australia

    Seyedali Mirjalili

  2. King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan

    Hossam Faris

  3. King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan

    Ibrahim Aljarah

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Al-Zoubi, A.M., Heidari, A.A., Habib, M., Faris, H., Aljarah, I., Hassonah, M.A. (2020). Salp Chain-Based Optimization of Support Vector Machines and Feature Weighting for Medical Diagnostic Information Systems. In: Mirjalili, S., Faris, H., Aljarah, I. (eds) Evolutionary Machine Learning Techniques. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-32-9990-0_2

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