Page 57 - 2023-Vol19-Issue2

53 | Hashim & Yassin

[13] Z. Huang and D. Chen, “A breast cancer diagnosis [25] L. Rokach and O. Maimon, “Decision trees,” Data Min.
method based on vim feature selection and hierarchi- Knowl. Discov. handbook. Springer, Boston, MA, no. Jan-
cal clustering random forest algorithm,” IEEE Access, uary, pp. 165–192, 2005.
vol. 10, pp. 3284–3293, 2022.
[26] M. A. Khan, M. A. Khan Khattk, S. Latif, A. A. Shah,
[14] J. Jumanto, M. F. Mardiansyah, R. N. Pratama, M. F. M. Ur Rehman, W. Boulila, M. Driss, and J. Ahmad, Vot-
Al Hakim, and B. Rawat, “Optimization of breast cancer ing classifier-based intrusion detection for iot networks.
classification using feature selection on neural network,” 2022.
Journal of Soft Computing Exploration, vol. 3, no. 2,
pp. 105–110, 2022.

[15] D. W. H. Wolberg,
“https://archive.ics.uci.edu/ml/datasets/breast can-
cer wisconsin (diagnostic),” M.L Repos., 1995.

[16] K. Teh, P. Armitage, S. Tesfaye, D. Selvarajah, and I. D.
Wilkinson, “Imbalanced learning: Improving classifi-
cation of diabetic neuropathy from magnetic resonance
imaging,” PloS one, vol. 15, no. 12, p. e0243907, 2020.

[17] K. Potdar, “A comparative study of categorical variable
encoding techniques for neural network classifiers,” Int.
J. Comput. Appl, vol. 175, no. 4, pp. 7–9, 2017.

[18] Q. Al-Tashi, S. J. Abdulkadir, H. M. Rais, S. Mirjalili,
and H. Alhussian, “Approaches to multi-objective fea-
ture selection: A systematic literature review,” IEEE
Access, vol. 8, pp. 125076–125096, 2020.

[19] R. Saidi, W. Bouaguel, and N. Essoussi, “Hybrid fea-
ture selection method based on the genetic algorithm
and pearson correlation coefficient,” Machine learning
paradigms: theory and application, pp. 3–24, 2019.

[20] B. Gierlichs and E. Prouff, “Mutual information analysis:
a comprehensive study mutual information analysis: a
comprehensive study,” J. Cryptol, vol. 24, no. 2, pp. 269–
291, 2011.

[21] A. Alonso-betanzos, “Filter methods for feature selec-
tion – a comparative study filter methods for feature
selection . a comparative study,” Int. Conf. Intell. Data
Eng. Autom. Learn. Springer, Berlin, Heidelb., vol. 4881,
no. December, pp. 178–187, 2007.

[22] P. Ferreira, D. C. Le, and N. Zincir-Heywood, “Explor-
ing feature normalization and temporal information for
machine learning based insider threat detection,” in 2019
15th International Conference on Network and Service
Management (CNSM), pp. 1–7, IEEE, 2019.

[23] W. T. Ambrosius, Topics in biostatistics. Springer, 2007.

[24] G. H. Lewes, “Support vector machines for classifica-
tion,” Effic. Learn. Mach. Apress, Berkeley, CA, no. Jan-
uary, pp. 39–66, 2015.