Open Access
Issue
E3S Web Conf.
Volume 351, 2022
10th International Conference on Innovation, Modern Applied Science & Environmental Studies (ICIES’2022)
Article Number 01027
Number of page(s) 4
DOI https://doi.org/10.1051/e3sconf/202235101027
Published online 24 May 2022
  1. Bouazza, S.H.; Hamdi, N.; Zeroual, A.; Auhmani, K. Gene-expression-based cancer classification through feature selection with KNN and SVM classifiers. Intell. Syst. Comput. Vis. (ISCV) 2015, pp.1–6, 2015. [Google Scholar]
  2. Hira, Z.M.; Gillies, D.F. A review of feature selection and feature extraction methods applied to microarray data. Adv. Bioinform. 2015, 2015, 198363. [CrossRef] [Google Scholar]
  3. Yeh, J.-Y. Applying data mining techniques for cancer classification on gene expression data. Cybern. Syst. Int. J. 2008, 39, 583–602. [CrossRef] [Google Scholar]
  4. Baez, J.C.; Fritz, T.; Leinster, T. A characterization of entropy in terms of information loss. Entropy 2011, 13, 1945–1957. [CrossRef] [Google Scholar]
  5. Chen, L.; Wu, K.; Li, Y. A load-balancing algorithm based on maximum entropy methods in homogeneous clusters. Entropy 2014, 16, 5677–5697. [CrossRef] [Google Scholar]
  6. Ismail, A. Abdlerazek, S., and El-Henawy, I.M. “Development of Smart Healthcare System Based on Speech Recognition Using Support Vector Machine and Dynamic Time Warping.” Sustainability 12, no. 6, 2020, 2403. [Google Scholar]
  7. Okun, O. Feature Selection and Ensemble Methods for Bioinformatics: Algorithmic Classification and Implementations, Information Science Reference— Imprint; IGI Publishing: Hershey, PA, USA, 2011. [Google Scholar]
  8. Mirjalili, S.; Mirjalili, S.M.; Lewis, A. Grey wolf optimizer. Adv. Eng. Softw. 2014, 69, 46–61. [CrossRef] [Google Scholar]
  9. Mech, L.D. Alpha status, dominance, and division of labor in wolf packs. Can. J. Zool. 1999, 77, 1196–1203. [CrossRef] [Google Scholar]
  10. Kumar, D.S.; Sathyadevi, G.; Sivanesh, S. Decision support system for medical diagnosis using data mining. Int. J. Comput. Sci. Issues 2011, 8, 147–153. [Google Scholar]
  11. Ismail, A. El-Henawy, I. “Quantified self-using IoT wearable devices”, Springer, pp. 820–831,2017. [Google Scholar]
  12. V. Dimitrov, “Medical internet of things and big data in healthcare.” Healthcare informatics research”, Vol 22(3), Pp. 156–163, 2016. [Google Scholar]
  13. Pomeroy, S.L.; Tamayo, P.; Gaasenbeek, M.; Sturla, L. M.; Angelo, M.; McLaughlin, M.E.; Kim, J.Y.H.; Goumnerova, L.C.; Black, P.M.; Lau, C.; et al. Prediction of central nervous system embryonal tumor outcome based on gene expression. Nature 2002, 415,436. [CrossRef] [PubMed] [Google Scholar]
  14. Cho, S.-B.; Won, H.-H. Machine learning in DNA microarray analysis for cancer classification. Proc. First Asia-Pac. Bioinform. Conf. Bioinform. 2003,19, 2003. [Google Scholar]
  15. Isaksson, A.; Wallman, M.; Gransson, H.; Gustafsson, M. G. Cross-validation and bootstrapping are unreliable in small sample classification. Pattern Recognit. Lett. 2008, 29,1960–1965. [CrossRef] [Google Scholar]
  16. Bolôn-Canedo, V.; Sanchez-Marono, N.; Alonso-Betanzos, A. An ensemble of filters and classifiers for microarray data classification. Pattern Recognit. 2012, 45, 531–539. [CrossRef] [Google Scholar]
  17. Alonso-Gonzalez, C.J.; Moro-Sancho, Q.; Isaac, S.-H.; Arancha Varela-Arrabal, R. Microarray gene expression classification with few genes: Criteria to combine attribute selection and classification methods. Expert Syst. Appl. 2012, 39, 7270–7280. [CrossRef] [Google Scholar]
  18. Gunavathi, C.; Premalatha, K. Performance analysis of genetic algorithm with kNN and SVM for feature selection in tumor classification. Int. J. Comput. Electr. Autom. Control Inf. Eng. 2014, 8,1490–1497. [Google Scholar]
  19. Paul, A.S.; Jaya, M.; Chitrangada, D. Gene selection for designing optimal fuzzy rule base classifier by estimating missing value. Appl. Soft Comput. 2017, 55, 276–288. [CrossRef] [Google Scholar]
  20. Moteghaed, N.Y.; Maghooli, K. ; Garshasb, M. Improving Classification of Cancer and Mining [Google Scholar]
  21. https://archive.ics.uci.edu/ml/datasets/cardiotocography [Google Scholar]
  22. https://en.wikipedia.org/wiki/Artificial_neural_network#Components_of_ANNs [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.