References
-
Feltes, B. C., Chandelier, E. B., Grisci, B. I. & Dorn, M. CuMiDa (2019) : An Extensively Curated Microarray Database for Benchmarking and Testing of Machine Learning Approaches in Cancer Research. J. Comput. Biol. 26(4), 376–386. https://doi.org/10.1089/cmb.2018.0238 (2019).
-
SREEDEVI, E. & PRADEEP JANGIR, D. S. KUMAR, J. R., An Enhanced Early Detection and Risk Prediction Of Brain Tumors Using MRI-CT Scans With Deep Learning Technique. Journal Theoretical Appl. Inform. Technology, 102(21), 7780–7792 (2024).
-
Abdul Rasool Hassan, B., Mohammed, A. H., Hallit, S., Malaeb, D. & Hosseini, H. Exploring the role of artificial intelligence in chemotherapy development, cancer diagnosis, and treatment: Present achievements and future outlook. Front. Oncol. 15, 1475893. https://doi.org/10.3389/fonc.2025.1475893 (2025).
-
Salma, R. A. et al. Leveraging machine learning for effective breast cancer diagnosis. WSEAS Trans. Comput. Res. 13, 34–46. https://doi.org/10.37394/232018.2025.13.4 (2025).
-
Erdal, H. & Namli, E. Monthly streamflow prediction: The power of ensemble machine learning based decision support models. Int. J. Hydrol. Sci. Technol. 1(1), 1. https://doi.org/10.1504/ijhst.2022.10046854 (20233).
-
Ghorai, S., Mukherjee, A., Sengupta, S. & Dutta, P. K. Multicategory cancer classification from gene expression data by Multiclass NPPC Ensemble. In Proceedings of the International Conference on Systems in Medicine and Biology, 41–48. (2010). https://doi.org/10.1109/icsmb.2010.5735343
-
Bhandari, N., Walambe, R., Kotecha, K. & Khare, S. P. A comprehensive survey on computational learning methods for analysis of gene expression data. Front. Mol. Biosci. 9, 907150. https://doi.org/10.3389/fmolb.2022.907150 (2022).
-
Madhu, G., Mohamed, A. W., Kautish, S., Shah, M. A. & Ali, I. Intelligent diagnostic model for malaria parasite detection and classification using imperative inception-based capsule neural networks. Sci. Rep. 13(1), 13377. https://doi.org/10.1038/s41598-023-40317-z (2023).
-
Saeys, Y., Inza, I. & Larrañaga, P. A review of feature selection techniques. Bioinf. Bioinformatics 23(19), 2507–2517. https://doi.org/10.1093/bioinformatics/btm344 (2007).
-
Varma, S. & Simon, R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics 7, 91. https://doi.org/10.1186/1471-2105-7-91 (2006).
-
Feng, C., Zhang, Z. & Pal, N. R. A comprehensive study on feature selection in the wrapper framework. Int. J. Mach. Learn. Cybern. 11, 1603–1626 (2020).
-
Zhang, Z., Kuhn, M. & Lalonde, M. Feature selection strategies for high-dimensional data in bioinformatics.. Curr. Opin. Biotechnol. 73, 148–154 (2022).
-
Chandrashekar, G. & Sahin, F. A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16–28. https://doi.org/10.1016/j.compeleceng.2013.11.024 (2014).
-
Joseph, J. A. et al. Artificial Intelligence Method for Detecting Brain Cancer Using Advanced Intelligent Algorithms. In 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 1482–1487). IEEE. (2023)., July https://doi.org/10.1109/ICESC57686.2023.10193659
-
Heidari, A. A. et al. Harris Hawks optimization: Algorithm and applications. Future Gener. Comput. Syst. 97, 849–872. https://doi.org/10.1016/j.future.2019.02.028 (2019).
-
Ramadan, O. I. et al. Enhancing breast cancer classification based on BPSO feature selection and machine learning techniques. Eng. Technol. Appl. Sci. Res. 15(3), 23907–23916. https://doi.org/10.48084/etasr.10900 (2025).
-
Abatal, A. et al. Hybrid long short-term memory and decision tree model for optimizing patient volume predictions in emergency. International Journal of Electrical and Computer Engineering (IJECE) 15(1), 669–676. https://doi.org/10.11591/ijece.v15i1.pp669-676 (2025).
-
Al Sukhni, H. et al. Brain tumor detection: Integrating machine learning and deep learning for robust brain tumor classification. J. Intell. Syst. Internet Things https://doi.org/10.54216/JISIoT.150101 (2025).
-
Aljanabi, M., Shkoukani, M. & Hijjawi, M. Ground-level ozone prediction using machine learning techniques: A case study in Amman, Jordan. Int. J. Autom. Comput. 17(5), 667–677. https://doi.org/10.1007/s11633-020-1233-4 (2020).
-
Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J. & Scholkopf, B. Support vector machines. IEEE Intelligent Systems and their Applications 13(4), 18–28. https://doi.org/10.1109/5254.708428 (1998).
-
Abdel-Basset, M., Abdel-Fatah, L. & Sangaiah, A. K. Metaheuristic algorithms: A comprehensive review. Comput. Intell. multimedia big data cloud Eng. Appl. 185–231. https://doi.org/10.1016/B978-0-12-813314-9.00010-4 (2018).
-
Bohat, V. K., Hashim, F. A., Batra, H. & Abd Elaziz, M. Phototropic growth algorithm: A novel metaheuristic inspired from phototropic growth of plants. Knowl. Based Syst. 322, 113548. https://doi.org/10.1016/j.knosys.2025.113548 (2025).
-
Mezura-Montes, E., Velázquez-Reyes, J. & Coello Coello, C. A. A comparative study of differential evolution variants for global optimization. In Proceedings of the 8th annual conference on Genetic and Evolutionary Computation (pp. 485–492). (2006)., July https://doi.org/10.1145/1143997.1144086
-
Shukla, A. K. Chaos teaching learning-based algorithm for large-scale global optimization problem and its application. Concurrency Comput. Pract. Exp. https://doi.org/10.1002/cpe.6514 (2022).
-
Tripathi, D., Shukla, A. K. & Reddy, R. Multi-layer hybrid credit scoring model based on feature selection, ensemble learning, and ensemble classifier. Handbook of Research on Data Science for Effective Healthcare Practice and Administration. (2020). https://doi.org/10.4018/978-1-5225-9643-1.ch021
-
Shukla, A. K. Simultaneously feature selection and parameters optimization by teaching–learning and genetic algorithms for diagnosis of breast cancer. Int. J. Data Sci. Anal. https://doi.org/10.1007/s41060-024-00513-0 (2024).
-
Shukla, A. K., Singh, P. & Vardhan, M. Hybrid TLBO-GSA strategy for constrained and unconstrained engineering optimization functions. In Hybrid Metaheuristics Research and Applications. (2018). https://doi.org/10.1142/9789813270237_0002
-
Singh, R. K. & Sivabalakrishnan, M. Feature Selection of Gene Expression Data for Cancer Classification: A Review. Procedia Comput. Sci. 2015, 50, 52–57. (2015). https://doi.org/10.1016/j.procs.2015.04.060
-
Kilicarslan, S., Adem, K. & Celik, M. Diagnosis and classification of cancer using a hybrid ReliefF and convolutional neural network model. Med. Hypotheses 137, 109577. https://doi.org/10.1016/j.mehy.2020.109577 (2020).
-
Elemam, T. & Elshrkawey, M. A Highly Discriminative Hybrid Feature Selection Algorithm for Cancer Diagnosis. The Scientific World Journal, 2022. (2022). https://doi.org/10.1155/2022/1056490
-
Qaraad, M. et al. A hybrid feature selection optimization model for high-dimension data classification. IEEE Access 9, 42884–42895. https://doi.org/10.1109/ACCESS.2021.3065341 (2021).
-
Ali, W. & Saeed, F. Hybrid filter and genetic algorithm-based feature selection for improving cancer classification in high-dimensional microarray data. Processes 11(2), 562. https://doi.org/10.3390/pr11020562 (2023).
-
Debata, P. P. & Mohapatra, P. A hybrid convolutional neural network approach for feature selection and disease classification. Turk. J. Electr. Eng. Comput. Sci. 29(8), 2580–2599. https://doi.org/10.3906/elk-2105-43 (2021).
-
Saeid, M. M., Nossair, Z. B. & Saleh, M. A. A microarray cancer classification technique based on discrete wavelet transforms for data reduction and a genetic algorithm for feature selection. In 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) (pp. 857–861). IEEE. (2020)., June https://doi.org/10.1109/ICOEI48184.2020.9143024
-
Passi, K., Nour, A. & Jain, C. K. Markov blanket: Efficient strategy for feature subset selection method for high dimensional microarray cancer datasets. In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 1864–1871). IEEE. (2017)., November https://doi.org/10.1109/BIBM.2017.8217942
-
Pashaei, E. & Pashaei, E. Hybrid binary COOT algorithm with simulated annealing for feature selection in high-dimensional microarray data. Neural Comput. Appl. 35(1), 353–374. https://doi.org/10.1007/s00521-022-07780-7 (2023).
-
Yaqoob, A., Verma, N. K. & Aziz, R. M. Optimizing gene selection and cancer classification with hybrid sine cosine and cuckoo search algorithm. J. Med. Syst. 48(1), 10. https://doi.org/10.1007/s10916-023-02031-1 (2024).
-
Vatankhah, M. & Momenzadeh, M. Self-regularized Lasso is used to select the most informative features in microarray cancer classification. Multimedia Tools Appl. 83(2), 5955–5970. https://doi.org/10.1007/s11042-023-15207-1 (2024).
-
Shiny, K. V. Brain tumor segmentation and classification using optimized U-Net. Imaging Sci. J. 72(2), 204–219. https://doi.org/10.1080/13682199.2023.2200614 (2024).
-
Hira, Z. M. & Gillies, D. F. A review of feature selection and feature extraction methods applied to microarray data. Advances in bioinformatics, 2015. (2015). https://doi.org/10.1155/2015/198363
-
Das, S. Filters, wrappers, and a boosting-based hybrid for feature selection. In Icml (Vol. 1, pp. 74–81). (2001), June.
-
Agrawal, P., Abutarboush, H. F., Ganesh, T. & Mohamed, A. W. Metaheuristic algorithms on feature selection: A survey of one decade of research (2009–2019). Ieee Access 9, 26766–26791. https://doi.org/10.1109/ACCESS.2021.3056407 (2021).
-
Arasteh, B., Sadegi, R., Aghaei, B. & Ghanbarzadeh, R. Single and multi-objective metaheuristic algorithms and their applications in software maintenance. Decision-Making Models. 97–110. https://doi.org/10.1016/B978-0-443-16147-6.00010-4 (2024).
-
Tawhid, M. A. & Ibrahim, A. M. Feature selection is based on a rough set approach, wrapper approach, and binary whale optimization algorithm. Int. J. Mach. Learn. Cybern. 11, 573–602. https://doi.org/10.1007/s13042-019-00996-5 (2020).
-
Xue, B., Zhang, M., Browne, W. N. & Yao, X. A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20(4), 606–626 (2015).
-
El Akadi, A., Amine, A., El Ouardighi, A. & Aboutajdine, D. A two-stage gene selection scheme utilizing MRMR filter and GA wrapper. Knowl. Inf. Syst. 26, 487–500. https://doi.org/10.1007/s10115-010-0288-x (2011).
-
Yan, X. & Jia, M. Intelligent fault diagnosis of rotating machinery using improved multiscale dispersion entropy and mRMR feature selection. Knowl. Based Syst. 163, 450–471. https://doi.org/10.1016/j.knosys.2018.09.004 (2019).
-
Shen, L. et al. Evolving support vector machines using fruit fly optimization for medical data classification. Knowl. Based Syst. 96, 61–75. https://doi.org/10.1016/j.knosys.2016.01.002 (2016).
-
Faris, H. et al. An efficient binary salp swarm algorithm with crossover scheme for feature selection problems. Knowl. Based Syst. 154, 43–67. https://doi.org/10.1016/j.knosys.2018.05.009 (2018).
-
Kumar, M., Kulkarni, A. J. & Satapathy, S. C. Socio evolution & learning optimization algorithm: A socio-inspired optimization methodology. Future Gener. Comput. Syst. 81, 252–272. https://doi.org/10.1016/j.future.2017.10.052 (2018).
-
Rao, R. V., Savsani, V. J. & Vakharia, D. P. Teaching–learning-based optimization: An optimization method for continuous non-linear large-scale problems. Inf. Sci. 183(1), 1–15. https://doi.org/10.1016/j.ins.2011.08.006 (2012).
-
Tamimi, E., Ebadi, H. & Kiani, A. Evaluation of different metaheuristic optimization algorithms in feature selection and parameter determination in SVM classification. Arab. J. Geosci. 10, 1–19. https://doi.org/10.1007/s12517-017-3254-z (2017).
-
Zhou, J. et al. Optimization of support vector machine through metaheuristic algorithms in forecasting TBM advance rate. Eng. Appl. Artif. Intell. 97, 104015. https://doi.org/10.1016/j.engappai.2020.104015 (2021).
-
Ardjani, F., Sadouni, K. & Benyettou, M. Optimization of SVM multiclass by particle swarm (PSO-SVM). In 2010 2nd International Workshop on Database Technology and Applications (pp. 1–4). IEEE. (2010)., November https://doi.org/10.5815/ijmecs.2010.02.05
-
Indraswari, R. & Arifin, A. Z. RBF kernel optimization method with particle swarm optimization on SVM using the analysis of input data’s movement. Jurnal Ilmu Komputer dan Informasi 10(1), 36–42. https://doi.org/10.21609/jiki.v10i1.410 (2017).
-
GSE50161. GEO accession in CuMiDa (Curated Microarray Database), brain cancer gene expression; 130 samples, five classes, 54,676 probes. URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE50161
-
Onyije, F. M. et al. Risk factors for childhood brain tumors: A systematic review and meta-analysis of observational studies from 1976 to 2022. Cancer Epidemiol. 88, 102510. https://doi.org/10.1016/j.canep.2023.102510 (2024).
-
Das, P. & Das, A.
Multi-scale cross spectral coherence and phase spectral distribution based measurement in non-subsampled shearlet domain for classification of brain tumors. Expert Syst. Appl. 247, 123329. https://doi.org/10.1016/j.eswa.2024.123329 (2024). -
Hosack, D. A., Dennis, G. Jr., Sherman, B. T., Lane, H. C. & Lempicki, R. A. Identifying biological themes within lists of genes with EASE. Genome Biol. 4(10), R70. https://doi.org/10.1186/gb-2003-4-10-r70 (2003).
-
Dennis, G. Jr. et al. DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 4(9), R60. https://doi.org/10.1186/gb-2003-4-9-r60 (2003).
-
Uzma, Al-Obeidat, F., Tubaishat, A., Shah, B. & Halim, Z. Gene encoder: a feature selection technique through unsupervised deep learning-based clustering for large gene expression data. Neural Comput. Appl. 34 (11), 8309–8331. https://doi.org/10.1007/s00521-020-05101-4 (2022).
-
El-Kafrawy, P., Manhrawy, I. I., Fathi, H., Qaraad, M. & Kelany, A. K. Using multi-feature selection with machine learning for de novo acute myeloid leukemia in Egypt. In 2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS) (pp. 1–8). IEEE. (2019)., December https://doi.org/10.1109/ISACS48493.2019.9068905
-
Halim, Z. An ensemble filter-based heuristic approach for cancerous gene expression classification. Knowl.-Based Syst. 234, 107560. https://doi.org/10.1016/j.knosys.2021.107560 (2021).
-
Abualigah, L., Al-Okbi, N. K., Mirjalili, S., Alshinwan, M., Al Hamad, H., Khasawneh,A. M., … Gandomi, A. H. (2022). Moth-Flame optimization Algorithm, arithmetic optimization Algorithm, Aquila Optimizer, Gray Wolf Optimizer, and sine cosine algorithm: a comparative analysis using multilevel thresholding image segmentation problems. In Handbook of Moth-Flame Optimization Algorithm (pp. 241–263). CRC Press. https://doi.org/10.1201/9781003205326.
-
Shannaq, F. et al. Exploring metaheuristic optimization algorithms in the context of textual cyberharassment: A systematic review. Expert Syst. 42(2), e13826. https://doi.org/10.1111/exsy.13826 (2025).
