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Bi-objective feature selection in high-dimensional datasets using improved binary chimp optimization algorithm

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Abstract

The machine learning process in high-dimensional datasets is far more complicated than in low-dimensional datasets. In high-dimensional datasets, Feature Selection (FS) is necessary to decrease the complexity of learning. However, FS in high-dimensional datasets is a complex process that requires the combination of several search techniques. The Chimp Optimization Algorithm, known as ChOA, is a new meta-heuristic method inspired by the chimps’ individual intellect and sexual incentive in cooperative hunting. It is basically employed in solving complex continuous optimization problems, while its binary version is frequently utilized in solving difficult binary optimization problems. Both versions of ChOA are subject to premature convergence and are incapable of effectively solving high-dimensional optimization problems. This paper proposes the Binary Improved ChOA Algorithm (BICHOA) for solving the bi-objective, high-dimensional FS problems (i.e., high-dimensional FS problems that aim to maximize the classifier’s accuracy and minimize the number of selected features from a dataset). BICHOA improves the performance of ChOA using four new exploration and exploitation techniques. First, it employs the opposition-based learning approach to initially create a population of diverse binary feasible solutions. Second, it incorporates the Lévy mutation function in the main probabilistic update function of ChOA to boost its searching and exploring capabilities. Third, it uses an iterative exploration technique based on an exploratory local search method called the \(\beta\)-hill climbing algorithm. Finally, it employs a new binary time-varying transfer function to calculate binary feasible solutions from the continuous feasible solutions generated by the update equations of the ChOA and \(\beta\)-hill climbing algorithms. BICHOA’s performance was assessed and compared against six machine learning classifiers, five integer programming methods, and nine efficient popular optimization algorithms using 25 real-world high-dimensional datasets from various domains. According to the overall experimental findings, BICHOA scored the highest accuracy, best objective value, and fewest selected features for each of the 25 real-world high-dimensional datasets. Besides, the reliability of the experimental findings was established using Friedman and Wilcoxon statistical tests.

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References

  1. Li K, Chen C-Y, Zhang Z-L (2023) Mining online reviews for ranking products: a novel method based on multiple classifiers and interval-valued intuitionistic fuzzy TOPSIS. Appl Soft Comput 139:110237

    Google Scholar 

  2. Abed-Alguni BH, Alawad NA, Al-Betar MA, Paul D (2023) Opposition-based sine cosine optimizer utilizing refraction learning and variable neighborhood search for feature selection. Appl Intell 53(11):13224–13260

    Google Scholar 

  3. Karizaki AA, Tavassoli M (2019) A novel hybrid feature selection based on ReliefF and binary dragonfly for high dimensional datasets. In: 2019 9th international conference on computer and knowledge engineering (ICCKE). IEEE, pp 300–304

  4. Abasabadi S, Nematzadeh H, Motameni H, Akbari E (2022) Hybrid feature selection based on SLI and genetic algorithm for microarray datasets. J Supercomput 78(18):19725–19753

    Google Scholar 

  5. Karimi F, Dowlatshahi MB, Hashemi A (2023) SemiACO: a semi-supervised feature selection based on ant colony optimization. Expert Syst Appl 214:119130

    Google Scholar 

  6. Xue Yu, Zhu H, Neri F (2023) A feature selection approach based on NSGA-II with ReliefF. Appl Soft Comput 134:109987

    Google Scholar 

  7. 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 

  8. Zhang B, Yang X, Biao H, Liu Z, Li Z (2020) OEbBOA: a novel improved binary butterfly optimization approaches with various strategies for feature selection. IEEE Access 8:67799–67812

    Google Scholar 

  9. Abed-alguni BH, AL-Jarah SH (2023) IBJA: an improved binary DJaya algorithm for feature selection. J Comput Sci 75:102201

    Google Scholar 

  10. Alawad NA, Abed-alguni BH, Al-Betar MA, Jaradat A (2023) Binary improved white shark algorithm for intrusion detection systems. Neural Comput Appl 35(26):19427–19451

    Google Scholar 

  11. Barhoush M, Abed-alguni BH, Al-qudah NEA (2023) Improved discrete salp swarm algorithm using exploration and exploitation techniques for feature selection in intrusion detection systems. J Supercomput 79(18):21265–21309

    Google Scholar 

  12. Elaziz MA, Oliva D (2018) Parameter estimation of solar cells diode models by an improved opposition-based whale optimization algorithm. Energy Convers Manag 171:1843–1859

    Google Scholar 

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

    Google Scholar 

  14. Awadallah MA, Braik MS, Al-Betar MA, Doush IA (2023) An enhanced binary artificial rabbits optimization for feature selection in medical diagnosis. Neural Comput Appl 35(27):20013–20068

    Google Scholar 

  15. Braik MS, Hammouri AI, Awadallah MA, Al-Betar MA, Khtatneh K (2023) An improved hybrid chameleon swarm algorithm for feature selection in medical diagnosis. Biomed Signal Process Control 85:105073

    Google Scholar 

  16. Mohamed EA, Braik MS, Al-Betar MA, Awadallah MA (2024) Boosted spider wasp optimizer for high-dimensional feature selection. J Bionic Eng. https://doi.org/10.1007/s42235-024-00558-8

    Article  Google Scholar 

  17. 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 

  18. Giraud C (2021) Introduction to high-dimensional statistics. Chapman and Hall/CRC, London

    Google Scholar 

  19. Yan C, Ma J, Luo H, Patel A (2019) Hybrid binary coral reefs optimization algorithm with simulated annealing for feature selection in high-dimensional biomedical datasets. Chemom Intell Lab Syst 184:102–111

    Google Scholar 

  20. Khishe M, Mosavi MR (2020) Chimp optimization algorithm. Expert Syst Appl 149:113338

    Google Scholar 

  21. Pashaei E, Pashaei E (2022) An efficient binary chimp optimization algorithm for feature selection in biomedical data classification. Neural Comput Appl 34(8):6427–6451

    Google Scholar 

  22. Kaidi W, Khishe M, Mohammadi M (2022) Dynamic levy flight chimp optimization. Knowl Based Syst 235:107625

    Google Scholar 

  23. Al-Betar MA, Awadallah MA, Bolaji AL, Alijla BO (2017) \(\beta\)-Hill climbing algorithm for sudoku game. In: 2017 Palestinian international conference on information and communication technology (PICICT). IEEE, pp 84–88

  24. Črepinšek M, Liu S-H, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv (CSUR) 45(3):1–33

    Google Scholar 

  25. Morales-Castañeda B, Zaldivar D, Cuevas E, Fausto F, Rodríguez A (2020) A better balance in metaheuristic algorithms: does it exist? Swarm Evol Comput 54:100671

    Google Scholar 

  26. Arora S, Singh S (2019) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23:715–734

    Google Scholar 

  27. Long W, Ming X, Jiao J, Tiebin W, Tang M, Cai S (2022) A velocity-based butterfly optimization algorithm for high-dimensional optimization and feature selection. Expert Syst Appl 201:117217

    Google Scholar 

  28. Zhu Y, Li W, Li T (2023) A hybrid artificial immune optimization for high-dimensional feature selection. Knowl Based Syst 260:110111

    Google Scholar 

  29. Ma W, Zhou X, Zhu H, Li L, Jiao L (2021) A two-stage hybrid ant colony optimization for high-dimensional feature selection. Pattern Recogn 116:107933

    Google Scholar 

  30. Kabir MM, Shahjahan Md, Murase K (2012) A new hybrid ant colony optimization algorithm for feature selection. Expert Syst Appl 39(3):3747–3763

    Google Scholar 

  31. Tabakhi S, Moradi P, Akhlaghian F (2014) An unsupervised feature selection algorithm based on ant colony optimization. Eng Appl Artif Intell 32:112–123

    Google Scholar 

  32. Tabakhi S, Moradi P (2015) Relevance-redundancy feature selection based on ant colony optimization. Pattern Recogn 48(9):2798–2811

    Google Scholar 

  33. Hatta NM, Zain AM, Roselina Sallehuddin Z, Shayfull YY (2019) Recent studies on optimisation method of grey wolf optimiser (GWO): a review (2014–2017). Artif Intell Rev 52:2651–2683

    Google Scholar 

  34. Pan H, Chen S, Xiong H (2023) A high-dimensional feature selection method based on modified gray wolf optimization. Appl Soft Comput 135:110031

    Google Scholar 

  35. Pant M, Zaheer H, Garcia-Hernandez L, Abraham A et al (2020) Differential evolution: a review of more than two decades of research. Eng Appl Artif Intell 90:103479

    Google Scholar 

  36. Mafarja M, Thaher T, Too J, Chantar H, Turabieh H, Houssein EH, Emam MM (2023) An efficient high-dimensional feature selection approach driven by enhanced multi-strategy grey wolf optimizer for biological data classification. Neural Comput Appl 35(2):1749–1775

    Google Scholar 

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

    Google Scholar 

  38. Rizk-Allah RM, Hassanien AE (2023) A comprehensive survey on the sine-cosine optimization algorithm. Artif Intell Rev 56(6):4801–4858

    Google Scholar 

  39. Abualigah L, Diabat A (2021) Advances in sine cosine algorithm: a comprehensive survey. Artif Intell Rev 54(4):2567–2608

    Google Scholar 

  40. 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 

  41. Mafarja M, Heidari AA, Habib M, Faris H, Thaher T, Aljarah I (2020) Augmented whale feature selection for IoT attacks: structure, analysis and applications. Future Gener Comput Syst 112:18–40

    Google Scholar 

  42. Dahou A, Elaziz MA, Chelloug SA, Awadallah MA, Al-Betar MA, Al-Qaness MAA, Forestiero A (2022) Intrusion detection system for IoT based on deep learning and modified reptile search algorithm. Comput Intell Neurosci. https://doi.org/10.1155/2022/6473507

    Article  Google Scholar 

  43. Abualigah L, Shehab M, Alshinwan M, Alabool H (2020) Salp swarm algorithm: a comprehensive survey. Neural Comput Appl 32:11195–11215

    Google Scholar 

  44. Khanesar MA, Teshnehlab M, Shoorehdeli MA (2007) A novel binary particle swarm optimization. In: 2007 Mediterranean conference on control & automation. IEEE, pp 1–6

  45. Wang G-G, Deb S, Cui Z (2019) Monarch butterfly optimization. Neural Comput Appl 31:1995–2014

    Google Scholar 

  46. Sun L, Si S, Zhao J, Jiucheng X, Lin Y, Lv Z (2023) Feature selection using binary monarch butterfly optimization. Appl Intell 53(1):706–727

    Google Scholar 

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

    Google Scholar 

  48. Thaher T, Heidari AA, Mafarja M, Dong JS, Mirjalili S (2020) Binary Harris hawks optimizer for high-dimensional, low sample size feature selection. In: Evolutionary machine learning techniques: algorithms and applications. pp 251–272

  49. Abed-Alguni BH, Paul D, Hammad R (2022) Improved salp swarm algorithm for solving single-objective continuous optimization problems. Appl Intell 52(15):17217–17236

    Google Scholar 

  50. Hussain K, Neggaz N, Zhu W, Houssein EH (2021) An efficient hybrid sine-cosine Harris hawks optimization for low and high-dimensional feature selection. Expert Syst Appl 176:114778

    Google Scholar 

  51. Balakrishnan K, Dhanalakshmi R, Akila M, Sinha BB (2023) Improved equilibrium optimization based on levy flight approach for feature selection. Evol Syst 14(4):735–746

    Google Scholar 

  52. Sayed S, Nassef M, Badr A, Farag I (2019) A nested genetic algorithm for feature selection in high-dimensional cancer microarray datasets. Expert Syst Appl 121:233–243

    Google Scholar 

  53. 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 

  54. 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 147:105675

    Google Scholar 

  55. Dhiman G, Garg M, Nagar A, Kumar V, Dehghani M (2021) A novel algorithm for global optimization: rat swarm optimizer. J Ambient Intell Humaniz Comput 12:8457–8482

    Google Scholar 

  56. Seyyedabbasi A, Kiani F (2021) I-GWO and Ex-GWO: improved algorithms of the grey wolf optimizer to solve global optimization problems. Eng Comput 37(1):509–532

    Google Scholar 

  57. Nadimi-Shahraki MH, Moeini E, Taghian S, Mirjalili S (2023) Discrete improved grey wolf optimizer for community detection. J Bionic Eng 20(5):2331–2358

    Google Scholar 

  58. Nadimi-Shahraki MH, Taghian S, Mirjalili S, Faris H (2020) MTDE: an effective multi-trial vector-based differential evolution algorithm and its applications for engineering design problems. Appl Soft Comput 97:106761

    Google Scholar 

  59. Nadimi-Shahraki MH, Taghian S, Zamani H, Mirjalili S, Elaziz MA (2023) MMKE: multi-trial vector-based monkey king evolution algorithm and its applications for engineering optimization problems. PLoS ONE 18(1):e0280006

    Google Scholar 

  60. Bernardino HS, Barbosa HJC (2009) Artificial immune systems for optimization. In: Nature-inspired algorithms for optimisation. Springer, pp 389–411

  61. Abdel-Basset M, Mohamed R, Jameel M, Abouhawwash M (2023) Spider wasp optimizer: a novel meta-heuristic optimization algorithm. Artif Intell Rev 56(10):11675–11738

    Google Scholar 

  62. Brownlee J (2020) Data preparation for machine learning: data cleaning, feature selection, and data transforms in Python. Machine Learning Mastery

  63. Alhenawi E, Alazzam H, Al-Sayyed R, AbuAlghanam O, Adwan O (2022) Hybrid feature selection method for intrusion detection systems based on an improved intelligent water drop algorithm. Cybern Inf Technol 22(4):73–90

    Google Scholar 

  64. Dong G, Liu H (2018) Feature engineering for machine learning and data analytics. CRC Press, Boca Raton

    Google Scholar 

  65. Al-Betar MA, Aljarah I, Awadallah MA, Faris H, Mirjalili S (2019) Adaptive \(\beta\)-hill climbing for optimization. Soft Comput 23(24):13489–13512

    Google Scholar 

  66. Abed-alguni BH, Alawad NA, Barhoush M, Hammad R (2021) Exploratory cuckoo search for solving single-objective optimization problems. Soft Comput 25(15):10167–10180

    Google Scholar 

  67. Shehab M, Khader AT, Al-Betar MA (2017) A survey on applications and variants of the cuckoo search algorithm. Appl Soft Comput 61:1041–1059

    Google Scholar 

  68. Gandomi AH, Yang X-S, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29:17–35

    Google Scholar 

  69. Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC’06), vol 1. IEEE, pp 695–701

  70. Li J, Gao L, Li X (2024) Multi-operator opposition-based learning with the neighborhood structure for numerical optimization problems and its applications. Swarm Evol Comput 84:101457

    Google Scholar 

  71. Islam MJ, Li X, Mei Y (2017) A time-varying transfer function for balancing the exploration and exploitation ability of a binary PSO. Appl Soft Comput 59:182–196

    Google Scholar 

  72. 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(7):7637–7665

    Google Scholar 

  73. Singh D, Singh B (2022) Feature wise normalization: an effective way of normalizing data. Pattern Recogn 122:108307

    Google Scholar 

  74. Fukunaga K (2013) Introduction to statistical pattern recognition. Elsevier, Amsterdam

    Google Scholar 

  75. Izonin I, Tkachenko R, Shakhovska N, Ilchyshyn B, Singh KK (2022) A two-step data normalization approach for improving classification accuracy in the medical diagnosis domain. Mathematics 10(11):1942

  76. Blagus R, Lusa L (2013) Smote for high-dimensional class-imbalanced data. BMC Bioinform 14:1–16

    Google Scholar 

  77. Allouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol 43(6):1223–1232

    Google Scholar 

  78. Mirjalili S, Zhang H, Mirjalili S, Chalup S, Noman N (2020) A novel u-shaped transfer function for binary particle swarm optimisation. In: Soft computing for problem solving 2019: proceedings of SocProS 2019, vol 1. Springer, pp 241–259

  79. Guo S, Wang J, Guo M (2020) Z-shaped transfer functions for binary particle swarm optimization algorithm. Comput Intell Neurosci 2020(1):6502807

    Google Scholar 

  80. Faris H, Heidari AA, Ala’M A-Z, Mafarja M, Aljarah I, Eshtay M, Mirjalili S (2020) Time-varying hierarchical chains of salps with random weight networks for feature selection. Expert Syst Appl 140:112898

    Google Scholar 

  81. Too J, Abdullah AR, Saad NM (2019) A new quadratic binary Harris hawk optimization for feature selection. Electronics 8(10):1130

    Google Scholar 

  82. Abdel-Basset M, El-Shahat D, El-Henawy I, De Victor Hugo C, Albuquerque SM (2020) A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection. Expert Syst Appl 139:112824

    Google Scholar 

  83. Shambour MKY, Abusnaina AA, Alsalibi AI (2019) Modified global flower pollination algorithm and its application for optimization problems. Interdiscip Sci Comput Life Sci 11:496–507

    Google Scholar 

  84. Li Z (2023) A local opposition-learning golden-sine grey wolf optimization algorithm for feature selection in data classification. Appl Soft Comput 142:110319

    Google Scholar 

  85. Sindhu R, Ngadiran R, Yacob YM, Zahri NAH, Hariharan M (2017) Sine-cosine algorithm for feature selection with elitism strategy and new updating mechanism. Neural Comput Appl 28:2947–2958

    Google Scholar 

  86. Too J, Mirjalili S (2021) General learning equilibrium optimizer: a new feature selection method for biological data classification. Appl Artif Intell 35(3):247–263

    Google Scholar 

  87. Gibbons JD, Gibbons Fielden JD (1993) Nonparametric statistics: an introduction, number 90. Sage

  88. Zhou H, Wang X, Zhu R (2022) Feature selection based on mutual information with correlation coefficient. Appl Intell 52(5):5457–5474

    Google Scholar 

  89. Agrawal RK, Kaur B, Sharma S (2020) Quantum based whale optimization algorithm for wrapper feature selection. Appl Soft Comput 89:106092

    Google Scholar 

  90. Meenachi L, Ramakrishnan S (2020) Differential evolution and ACO based global optimal feature selection with fuzzy rough set for cancer data classification. Soft Comput 24(24):18463–18475

    Google Scholar 

  91. Piri J, Mohapatra P, Pradhan MR, Acharya B, Patra TK (2021) A binary multi-objective chimp optimizer with dual archive for feature selection in the healthcare domain. IEEE Access 10:1756–1774

    Google Scholar 

  92. Zhang X, Fan M, Wang D, Zhou P, Tao D (2020) Top-k feature selection framework using robust 0–1 integer programming. IEEE Trans Neural Netw Learn Syst 32(7):3005–3019

    MathSciNet  Google Scholar 

  93. Cai D, Zhang C, He X (2010) Unsupervised feature selection for multi-cluster data. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. pp 333–342

  94. He X, Cai D, Niyogi P (2005) Laplacian score for feature selection. In: Advances in neural information processing systems, vol 18

  95. Shi Y, Miao J, Wang Z, Zhang P, Niu L (2018) Feature selection with \(\backslash\)ell_ \(2, 1--2\) regularization. IEEE Trans Neural Netw Learn Syst 29(10):4967–4982

    MathSciNet  Google Scholar 

  96. Zhang Q, Sun J, Tsang E, Ford J (2004) Hybrid estimation of distribution algorithm for global optimization. Eng Comput 21(1):91–107

    Google Scholar 

  97. Nadimi-Shahraki MH, Taghian S, Mirjalili S, Abualigah L (2022) Binary aquila optimizer for selecting effective features from medical data: a covid-19 case study. Mathematics 10(11):1929

    Google Scholar 

  98. Sharafaldin I, Lashkari AH, Hakak S, Ghorbani AA (2019) Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy. In: 2019 international Carnahan conference on security technology (ICCST). IEEE, pp 1–8

  99. Nuiaa RR, Manickam S, Alsaeedi AH, Alomari ES (2022) A new proactive feature selection model based on the enhanced optimization algorithms to detect DRDoS attacks. Int J Electr Comput Eng 12(2):1869–1880

    Google Scholar 

  100. Abed-alguni BH, Barhoush M (2018) Distributed grey wolf optimizer for numerical optimization problems. Jordan J Comput Inf Technol 4(03):21

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Yarmouk University, Irbid, Jordan

    Nour Elhuda A. Al-qudah & Bilal H. Abed-alguni

  2. Department of Information Technology (Cybersecurity Program), Yarmouk University, Irbid, Jordan

    Malek Barhoush

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  1. Nour Elhuda A. Al-qudah
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Contributions

Nour Elhuda A. Al-qudah was contributed to conceptualization, methodology, investigation, validation, writing—original draft preparation, supervision. Bilal H. Abed-alguni was contributed to experimentation, visualization, writing—original draft preparation, reviewing and editing. Malek Barhoush was contributed to experimentation, visualization, reviewing and editing.

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Correspondence to Bilal H. Abed-alguni.

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Appendices

Appendix A: comparison between ML classifiers and BICHOA based on true skill statics

True Skill Statics (TSS) is one of the most efficient quantitative measures of performance. It can differentiate between two types of errors (omission and commission errors). The values of these errors range from − 1 to 1, where a value in [− 1,0] suggests that the performance is random and unreliable, and a value of 1 suggests perfect agreement (reliable performance). In Table 28, the TSS measure was used to compare between BICHOA and four ML approaches (SVM, KNN, DT, and LR). From the table, we can clearly make several observations. All TSS values are > 0, and most are 1’s or close to 1’s. This indicates that the performance of these algorithms is not random. Second, BICHOA scored the highest TSS values for 17 out of 25 datasets compared to the other ML classifiers. In addition, most of the TSS values of BICHOA are greater than 0.5, and most scores are 1’s or near 1’s.

Table 28 TSS values of BICHOA and some ML classifiers over high-dimensional datasets
Full size table

Appendix B: comparison between five ML classifiers embedded in BICHOA

In this experiment, four classifiers (SVM, KNN, DT, and LR) were embedded individually into BICHOA to produce four variations of BICHOA (BICHOA-SVM, BICHOA-KNN, BICHOA-DT, and BICHOA-LR). The purpose here is to test which ML classifier shows the best performance with BICHOA. The results in Table 29 are a summary of the overall results (Accuracy, Precision, Recall, F1 score) of four variations of BICHOA over 25 high-dimensional datasets. Overall, BICHOA-SVM was the best variation of BICHOA, where it scored the best results for 22 datasets out of 25 possible datasets. This simply suggests that SVM is the best choice of classifier for BICHOA. Hence, BICHOA-SVM was used for comparison purposes with the baselines in Sect. 5.

Table 29 Comparison between five ML classifiers embedded in BICHOA over high-dimensional datasets
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Al-qudah, N.E.A., Abed-alguni, B.H. & Barhoush, M. Bi-objective feature selection in high-dimensional datasets using improved binary chimp optimization algorithm. Int. J. Mach. Learn. & Cyber. 15, 6107–6148 (2024). https://doi.org/10.1007/s13042-024-02308-y

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