Skip to main content
SpringerLink
Log in

An enhanced binary artificial rabbits optimization for feature selection in medical diagnosis

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper proposes binary versions of artificial rabbits optimization (ARO) for feature selection (FS) with medical diagnosis data. ARO is a recent swarm-based optimization algorithm that mimics rabbits’ natural survival tactics and eating habits. It was modeled in an optimization context to tackle optimization problems of continuous search spaces. In this paper, ARO is improved to deal with the binary domain of FS. The improvements include three additions: First, different alternatives of transfer functions were used to convert ARO from continuous to binary; second, the global-best concept was added to the binary ARO to improve the exploitation capability of the proposed algorithm; and finally, Lévy flight and opposition-based learning strategies were injected into the proposed algorithm to enhance its diversity and thus improve the balance between global exploration and local exploitation during all stages of the search process. Six binary variants of ARO were designed across an extensive set of experiments to study the impact of using the proposed amendments on the performance of the proposed ARO algorithm. These variants are: binary ARO with S-shaped transfer function (BAROS), binary ARO with V-shaped transfer function (BAROV), BAROS with the global-best concept (BGAROS), BGAROV with the global-best concept (BGAROV), BGAROS with Lévy flight and opposition-based learning strategies (BGAROSLO), and BGAROV with Lévy flight and opposition-based learning strategies (BGAROVLO). The proposed binary ARO versions were evaluated using 23 medical FS datasets. In addition, the proposed algorithm was applied to detect coronavirus disease using a real COVID-19 dataset. Five performance measures were used: classification accuracy, sensitivity, specificity, fitness value, and the number of selected features. In a nutshell, the proposed binary ARO versions were able to achieve success rates for these performance metrics as follows: 66.7%, 50%, 33.3%, 66.7%, and 83.3%, respectively. In conclusion, the success of the proposed ARO versions was realized due to the suitable design of the parameters of the proposed ARO version, such as transfer functions, global-best concept, Lévy flight, and opposition-based learning strategies. A comprehensive comparative evaluation was studied against ten well-established methods using the same datasets with a high preference for the proposed ARO versions, especially BGAROSLO which can achieve the best accuracy for the majority of the FS datasets. This is proven using Friedman’s statistical test ad-hocked by Holm’s test.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Notes

  1. https://jundongl.github.io/scikit-feature/datasets.html

References

  1. Elminaam DSA, Nabil A, Ibraheem SA, Houssein EH (2021) An efficient marine predators algorithm for feature selection. IEEE Access 9:60136–60153

    Google Scholar 

  2. Abdel-Basset M, Ding W, El-Shahat D (2021) A hybrid harris hawks optimization algorithm with simulated annealing for feature selection. Artif Intell Rev 54(1):593–637

    Google Scholar 

  3. Abdollahzadeh B, Gharehchopogh FS (2022) A multi-objective optimization algorithm for feature selection problems. Eng Comput 38(3):1845–1863

    Google Scholar 

  4. Khurma RA, Aljarah I, Sharieh A, Elaziz MA, Damaševičius R, Krilavičius T (2022) A review of the modification strategies of the nature inspired algorithms for feature selection problem. Mathematics 10(3):464

    Google Scholar 

  5. Khurmaa RA, Aljarah I, Sharieh A (2021) An intelligent feature selection approach based on moth flame optimization for medical diagnosis. Neural Comput Appl 33(12):7165–7204

    Google Scholar 

  6. Abualigah L, Diabat A (2022) Chaotic binary group search optimizer for feature selection. Expert Syst Appl 192:116368

    Google Scholar 

  7. Ahmadianfar I, Bozorg-Haddad O, Chu X (2020) Gradient-based optimizer: a new metaheuristic optimization algorithm. Inf Sci 540:131–159

    MathSciNet  MATH  Google Scholar 

  8. Al-Betar MA, Alomari OA, Abu-Romman SM (2020) A triz-inspired bat algorithm for gene selection in cancer classification. Genomics 112(1):114–126

    Google Scholar 

  9. Al-Betar MA, Hammouri AI, Awadallah MA, Doush IA (2021) Binary \(\beta\)-hill climbing optimizer with s-shape transfer function for feature selection. J Ambient Intell Humaniz Comput 12:7637–7665

    Google Scholar 

  10. Albashish D, Hammouri AI, Braik M, Atwan J, Sahran S (2021) Binary biogeography-based optimization based svm-rfe for feature selection. Appl Soft Comput 101:107026

    Google Scholar 

  11. Salem Alelyani, Jiliang Tang, Huan Liu (2018) Feature selection for clustering: a review. Data Cluster 2:29–60

    Google Scholar 

  12. Alhenawi E, Al-Sayyed R, Hudaib A, Mirjalili S (2022) Feature selection methods on gene expression microarray data for cancer classification: a systematic review. Comput Biol Med 140:105051

    Google Scholar 

  13. Alomari OA, Makhadmeh SN, Al-Betar MA, Alyasseri ZAA, Doush IA, Abasi AK, Awadallah MA, Zitar RA (2021) Gene selection for microarray data classification based on gray wolf optimizer enhanced with triz-inspired operators. Knowl-Based Syst 223:107034

    Google Scholar 

  14. Alsahaf A, Petkov N, Shenoy V, Azzopardi G (2022) A framework for feature selection through boosting. Expert Syst Appl 187:115895

    Google Scholar 

  15. Alweshah M, Khalaileh SA, Gupta BB, Almomani A, Hammouri AI, Al-Betar MA (2020) The monarch butterfly optimization algorithm for solving feature selection problems. Neural Comput Appl 2:1–15

    Google Scholar 

  16. Alweshah M, Alkhalaileh S, Al-Betar MA, Bakar AA (2022) Coronavirus herd immunity optimizer with greedy crossover for feature selection in medical diagnosis. Knowl-Based Syst 235:107629

    Google Scholar 

  17. Alweshah M, Alkhalaileh S, Al-Betar MA, Bakar AA (2022) Coronavirus herd immunity optimizer with greedy crossover for feature selection in medical diagnosis. Knowl-Based Syst 235:107629

    Google Scholar 

  18. Arora S, Anand P (2019) Binary butterfly optimization approaches for feature selection. Expert Syst Appl 116:147–160

    Google Scholar 

  19. Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12

    Google Scholar 

  20. Awadallah MA, Al-Betar MA, Braik MS, Hammouri AI, Doush IA, Zitar RA (2022) An enhanced binary rat swarm optimizer based on local-best concepts of pso and collaborative crossover operators for feature selection. Comput Biol Med 2:105675

    Google Scholar 

  21. Awadallah MA, Al-Betar MA, Hammouri AI, Alomari OA (2020) Binary jaya algorithm with adaptive mutation for feature selection. Arab J Sci Eng 45(12):10875–10890

    Google Scholar 

  22. Awadallah MA, Hammouri AI, Al-Betar MA, Braik MS, Elaziz MA (2022) Binary horse herd optimization algorithm with crossover operators for feature selection. Comput Biol Med 141:105152

    Google Scholar 

  23. Bezdan T, Zivkovic M, Bacanin N, Chhabra A, Suresh M (2022) Feature selection by hybrid brain storm optimization algorithm for covid-19 classification. J Comput Biol 29(6):515–529

    MathSciNet  Google Scholar 

  24. Braik M (2022) Enhanced Ali Baba and the forty thieves algorithm for feature selection. Neural Comput Appl 2:1–32

    Google Scholar 

  25. Braik M, Al-Zoubi H, Ryalat M, Sheta A, Alzubi O (2023) Memory based hybrid crow search algorithm for solving numerical and constrained global optimization problems. Artif Intell Rev 56(1):27–99

    Google Scholar 

  26. Braik M, Hammouri A, Atwan J, Al-Betar MA, Awadallah MA (2022) White shark optimizer: a novel bio-inspired meta-heuristic algorithm for global optimization problems. Knowl-Based Syst 243:108457

    Google Scholar 

  27. Braik M, Ryalat MH, Al-Zoubi H (2022) A novel meta-heuristic algorithm for solving numerical optimization problems: Ali baba and the forty thieves. Neural Comput Appl 34(1):409–455

    Google Scholar 

  28. Braik MS (2021) Chameleon swarm algorithm: a bio-inspired optimizer for solving engineering design problems. Expert Syst Appl 174:114685

    Google Scholar 

  29. Chaudhuri A, Sahu TP (2021) Feature selection using binary crow search algorithm with time varying flight length. Expert Syst Appl 168:114288

    Google Scholar 

  30. Deng Z, Chung F-L, Wang S (2010) Robust relief-feature weighting, margin maximization, and fuzzy optimization. IEEE Trans Fuzzy Syst 18(4):726–744

    Google Scholar 

  31. Dokeroglu T, Deniz A, Kiziloz HE (2022) A comprehensive survey on recent metaheuristics for feature selection. Neurocomputing 5:269–296

    Google Scholar 

  32. Elgamal ZM, Yasin NBM, Tubishat M, Alswaitti M, Mirjalili S (2020) An improved harris hawks optimization algorithm with simulated annealing for feature selection in the medical field. IEEE Access 8:186638–186652

    Google Scholar 

  33. Emary E, Zawbaa HM, Hassanien AE (2016) Binary ant lion approaches for feature selection. Neurocomputing 213:54–65

    Google Scholar 

  34. Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020) Marine predators algorithm: a nature-inspired metaheuristic. Expert Syst Appl 152:113377

    Google Scholar 

  35. Faris H, Mafarja MM, Heidari AA, Aljarah I, Ala’M A-Z, Mirjalili S, Fujita H (2018) An efficient binary salp swarm algorithm with crossover scheme for feature selection problems. Knowl-Based Syst 154:43–67

    Google Scholar 

  36. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3(Mar):1157–1182

    MATH  Google Scholar 

  37. Habib M, Aljarah I, Faris H, Mirjalili S (2020) Multi-objective particle swarm optimization: theory, literature review, and application in feature selection for medical diagnosis. Evol Mach Learn Tech 5:175–201

    Google Scholar 

  38. Hanif Halim A, Ismail I, Das S (2021) Performance assessment of the metaheuristic optimization algorithms: an exhaustive review. Artif Intell Rev 54(3):2323–2409

    Google Scholar 

  39. Han F, Chen W-T, Ling Q-H, Han H (2021) Multi-objective particle swarm optimization with adaptive strategies for feature selection. Swarm Evol Comput 62:100847

    Google Scholar 

  40. Hassan MR, Huda S, Hassan MM, Abawajy J, Alsanad A, Fortino G (2022) Early detection of cardiovascular autonomic neuropathy: a multi-class classification model based on feature selection and deep learning feature fusion. Inf Fusion 77:70–80

    Google Scholar 

  41. 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

    Google Scholar 

  42. Gang H, Bo D, Wang X, Wei G (2021) An enhanced black widow optimization algorithm for feature selection. Knowl-Based Syst 2:107638

    Google Scholar 

  43. Gang H, Bo D, Wang X, Wei G (2022) An enhanced black widow optimization algorithm for feature selection. Knowl-Based Syst 235:107638

    Google Scholar 

  44. Jiao H, Gui W, Heidari AA, Cai Z, Liang G, Chen H, Pan Z (2022) Dispersed foraging slime mould algorithm: continuous and binary variants for global optimization and wrapper-based feature selection. Knowl-Based Syst 237:107761

    Google Scholar 

  45. Ying H, Zhang Y, Gong D (2020) Multiobjective particle swarm optimization for feature selection with fuzzy cost. IEEE Trans Cybern 51(2):874–888

    Google Scholar 

  46. Iwendi C, Bashir AK, Atharva Peshkar R, Sujatha JM, Chatterjee SP, Mishra R, Pillai S, Jo O (2020) Covid-19 patient health prediction using boosted random forest algorithm. Front Public Health 8:357

    Google Scholar 

  47. Ji B, Xiaozheng L, Sun G, Zhang W, Li J, Xiao Y (2020) Bio-inspired feature selection: an improved binary particle swarm optimization approach. IEEE Access 8:85989–86002

    Google Scholar 

  48. Joshi SK (2023) Chaos embedded opposition based learning for gravitational search algorithm. Appl Intell 53(5):5567–5586

    Google Scholar 

  49. Kale GA, Yüzgeç U (2022) Advanced strategies on update mechanism of sine cosine optimization algorithm for feature selection in classification problems. Eng Appl Artif Intell 107:104506

    Google Scholar 

  50. Kelidari M, Hamidzadeh J (2021) Feature selection by using chaotic cuckoo optimization algorithm with levy flight, opposition-based learning and disruption operator. Soft Comput 25(4):2911–2933

    Google Scholar 

  51. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-international conference on neural networks, vol 4, pp 1942–1948. IEEE

  52. Khurma RA, Albashish D, Braik M, Alzaqebah A, Qasem A, Adwan O (2023) An augmented snake optimizer for diseases and covid-19 diagnosis. Biomed Signal Process Control 84:104718

    Google Scholar 

  53. 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

    Google Scholar 

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

    Google Scholar 

  55. Mafarja M, Mirjalili S (2018) Whale optimization approaches for wrapper feature selection. Appl Soft Comput 62:441–453

    Google Scholar 

  56. Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302–312

    Google Scholar 

  57. Mahendran N, PM DRV (2022) A deep learning framework with an embedded-based feature selection approach for the early detection of the alzheimer’s disease. Comput Biol Med 141:105056

    Google Scholar 

  58. Maldonado S, López J (2018) Dealing with high-dimensional class-imbalanced datasets: embedded feature selection for svm classification. Appl Soft Comput 67:94–105

    Google Scholar 

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

    Google Scholar 

  60. Nadimi-Shahraki MH, Banaie-Dezfouli M, Zamani H, Taghian S, Mirjalili S (2021) B-mfo: a binary moth-flame optimization for feature selection from medical datasets. Computers 10(11):136

    Google Scholar 

  61. Nadimi-Shahraki MH, Zamani H, Mirjalili S (2022) Enhanced whale optimization algorithm for medical feature selection: a covid-19 case study. Comput Biol Med 148:105858

    Google Scholar 

  62. Neggaz N, Ewees AA, Elaziz MA, Mafarja M (2020) Boosting salp swarm algorithm by sine cosine algorithm and disrupt operator for feature selection. Expert Syst Appl 145:113103

    Google Scholar 

  63. Neggaz N, Houssein EH, Hussain K (2020) An efficient henry gas solubility optimization for feature selection. Expert Syst Appl 152:113364

    Google Scholar 

  64. Ovhal P, Kulkarni S, Valadi JK (2022) Improved filter ranking incorporated binary black hole algorithm for feature selection. SN Comput Sci 3(1):1–9

    Google Scholar 

  65. Rajammal RR, Mirjalili S, Ekambaram G, Palanisamy N (2022) Binary grey wolf optimizer with mutation and adaptive k-nearest neighbour for feature selection in Parkinson’s disease diagnosis. Knowl-Based Syst 246:108701

    Google Scholar 

  66. Ramírez-Gallego S, Lastra I, Martínez-Rego D, Bolón-Canedo V, Benítez JM, Herrera F, Alonso-Betanzos A (2017) Fast-mrmr: fast minimum redundancy maximum relevance algorithm for high-dimensional big data. Int J Intell Syst 32(2):134–152

    Google Scholar 

  67. Rao R (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34

    Google Scholar 

  68. Venkata Rao R, Savsani VJ, Vakharia DP (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315

    Google Scholar 

  69. Remeseiro B, Bolon-Canedo V (2019) A review of feature selection methods in medical applications. Comput Biol Med 112:103375

    Google Scholar 

  70. Ridha HM, Heidari AA, Wang M, Chen H (2020) Boosted mutation-based harris hawks optimizer for parameters identification of single-diode solar cell models. Energy Convers Manage 209:112660

    Google Scholar 

  71. Sahran S, Albashish D, Abdullah A, Shukor NA, Pauzi SHM (2018) Ab solute cosine-based svm-rfe feature selection method for prostate histopathological grading. Artif Intell Med 87:78–90

    Google Scholar 

  72. Saw T, Myint PH (2019) Feature selection to classify healthcare data using wrapper method with pso search. Int J Inf Technol Comput Sci (IJITCS) 11(9):31–37

    Google Scholar 

  73. Sharma A, Mishra PK (2022) Performance analysis of machine learning based optimized feature selection approaches for breast cancer diagnosis. Int J Inf Technol 14(4):1949–1960

    Google Scholar 

  74. Sihwail R, Omar K, Ariffin KAZ, Tubishat M (2020) Improved harris hawks optimization using elite opposition-based learning and novel search mechanism for feature selection. IEEE Access 8:121127–121145

    Google Scholar 

  75. Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713

    Google Scholar 

  76. Song X-F, Zhang Y, Gong D-W, Gao X-Z (2021) A fast hybrid feature selection based on correlation-guided clustering and particle swarm optimization for high-dimensional data. IEEE Trans Cybern 2:568

    Google Scholar 

  77. Tang J, Mou M, Wang Y, Luo Y, Zhu F (2021) Metafs: performance assessment of biomarker discovery in metaproteomics. Brief Bioinform 22(3):bbaa105

    Google Scholar 

  78. Taradeh M, Mafarja M, Heidari AA, Faris H, Aljarah I, Mirjalili S, Fujita H (2019) An evolutionary gravitational search-based feature selection. Inf Sci 497:219–239

    Google Scholar 

  79. Thaher T, Mafarja M, Turabieh H, Castillo PA, Faris H, Aljarah I (2021) Teaching learning-based optimization with evolutionary binarization schemes for tackling feature selection problems. IEEE Access 9:41082–41103

    Google Scholar 

  80. Too J, Liang G, Chen H (2021) Memory-based harris hawk optimization with learning agents: a feature selection approach. Eng Comput 2:1–22

    Google Scholar 

  81. Tubishat M, Idris N, Shuib L, Abushariah MAM, Mirjalili S (2020) Improved salp swarm algorithm based on opposition based learning and novel local search algorithm for feature selection. Expert Syst Appl 145:113122

    Google Scholar 

  82. Wang L, Cao Q, Zhang Z, Mirjalili S, Zhao W (2022) Artificial rabbits optimization: a new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Eng Appl Artif Intell 114:105082

    Google Scholar 

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

    Google Scholar 

  84. Xue B, Zhang M, Browne WN, Yao X (2015) A survey on evolutionary computation approaches to feature selection. IEEE Trans Evol Comput 20(4):606–626

    Google Scholar 

  85. Xue J, Shen B (2020) A novel swarm intelligence optimization approach: sparrow search algorithm. Syst Sci Control Eng 8(1):22–34

    Google Scholar 

  86. Yang Q, Li B, Tang J, Cui X, Wang Y, Li X, Jie H, Chen Y, Xue W, Lou Y et al (2020) Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data. Brief Bioinform 21(3):1058–1068

    Google Scholar 

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

    Google Scholar 

  88. Yang X-S, Deb S (2010) Engineering optimisation by cuckoo search. Int J Math Modell Numer Optim 1(4):330–343

    MATH  Google Scholar 

  89. Yildiz BS, Pholdee N, Bureerat S, Yildiz AR, Sait SM (2022) Enhanced grasshopper optimization algorithm using elite opposition-based learning for solving real-world engineering problems. Eng Comput 38(5):4207–4219

    Google Scholar 

  90. Zhang X, Yueting X, Caiyang Yu, Heidari AA, Li S, Chen H, Li C (2020) Gaussian mutational chaotic fruit fly-built optimization and feature selection. Expert Syst Appl 141:112976

    Google Scholar 

  91. Zhang Y, Gong D, Gao X, Tian T, Sun X (2020) Binary differential evolution with self-learning for multi-objective feature selection. Inf Sci 507:67–85

    MathSciNet  MATH  Google Scholar 

  92. Zhang Y, Song X, Gong D (2017) A return-cost-based binary firefly algorithm for feature selection. Inf Sci 418:561–574

    Google Scholar 

  93. Zhong C, Li G, Meng Z, He W (2023) Opposition-based learning equilibrium optimizer with levy flight and evolutionary population dynamics for high-dimensional global optimization problems. Expert Syst Appl 215:119303

    Google Scholar 

  94. Zitar RA, Al-Betar MA, Awadallah MA, Doush IA, Assaleh K (2022) An intensive and comprehensive overview of jaya algorithm, its versions and applications. Arch Comput Methods Eng 29:763–792

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza, Palestine

    Mohammed A. Awadallah

  2. Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, United Arab Emirates

    Mohammed A. Awadallah

  3. Department of Computer Science, Al-Balqa Applied University, Salt, Jordan

    Malik Shehadeh Braik

  4. Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates

    Mohammed Azmi Al-Betar

  5. Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Irbid, Jordan

    Mohammed Azmi Al-Betar

  6. Department of Computing, College of Engineering and Applied Sciences, American University of Kuwait, Salmiya, Kuwait

    Iyad Abu Doush

  7. Computer Science Department, Yarmouk University, Irbid, Jordan

    Iyad Abu Doush

Authors
  1. Mohammed A. Awadallah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Malik Shehadeh Braik
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammed Azmi Al-Betar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Iyad Abu Doush
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammed A. Awadallah.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Awadallah, M.A., Braik, M.S., Al-Betar, M.A. et al. An enhanced binary artificial rabbits optimization for feature selection in medical diagnosis. Neural Comput & Applic 35, 20013–20068 (2023). https://doi.org/10.1007/s00521-023-08812-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-08812-6

Keywords

Search

Navigation