66
Views
1
CrossRef citations to date
0
Altmetric
Articles

Statistical methods for feature selection: unlocking the key to improved accuracy

ORCID Icon &
Pages 433-443 | Received 13 Mar 2023, Accepted 07 Jun 2023, Published online: 15 Jun 2023

References

  • Sanchez-Marono N, Alonso-Betanzos A, Tombilla-Sanroman M. Filter methods for feature selection – a comparative study. Intell Data Eng Autom Learn (IDEAL). 2017;4881:178–187.
  • Farooqi N, Gutub A, Khozium O. Smart community challenges: enabling IoT/M2M technology case study. Life Sci J. 2019;16:11–17.
  • Thabtah F, Kamalov F, Hammoud S, et al. Least loss: a simplified filter method for feature selection. Inf Sci. 2020;534:1–15. doi:10.1016/j.ins.2020.05.017
  • Gunjan A, Tanvir A, Mohammad D. Hybrid filter–wrapper feature selection method for sentiment classification. Arab J Sci Eng. 2019;44:1–18.
  • Jeyauthmigha RK, Suganthe RC. Recursive feature elimination and clustering technique for network anomaly detection. 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT); Nov. 2018, p.1–6.
  • Singh A, Gutub A, Roy A, et al. AI-based mobile edge computing for IoT: applications, challenges, and future scope. Arab J Sci Eng (AJSE). 2022;47(8):9801–9831. doi:10.1007/s13369-021-06348-2
  • Qian W, Xiong Y, Yang J, et al. Feature selection for label distribution learning via feature similarity and label correlation. Inf Sci. 2022;582:38–59. doi:10.1016/j.ins.2021.08.076
  • Prasetiyowati MI, Maulidevi NU, Surendro K. Determining threshold value on information gain feature selection to increase speed and prediction accuracy of random forest. J Big Data. 2021;8:1–22. doi:10.1186/s40537-021-00472-4
  • Van SH, Ha NN, Thi Bao HN. A hybrid feature selection method for credit scoring. EAI Endorsed Transactions on Context-Aware Systems and Applications; 2017, p. 4.
  • Kamarudin MH, Maple C, Watson T. Hybrid feature selection technique for intrusion detection system. Int J High-Perform Comput Netw. 2019;13(2):232–240.
  • Dwivedi S, Vardhan M, Tripathi S. Defense against distributed DoS attack detection by using intelligent evolutionary algorithm. Int J Comput Appl. 2020;44(3):219–229. doi:10.1080/1206212X.2020.1720951
  • Kumar N, Singh AK, Srivastava S. Feature selection for interest flooding attack in named data networking. Int J Comput Appl. 2021;43(6):537–546. doi:10.1080/1206212X.2019.1583820
  • Samkari H, Gutub A. Protecting medical records against cybercrimes within Hajj period by 3-layer security. Recent Trends Inform Technol Appl. 2019;2(3):1–21.
  • Alassaf N, Alkazemi B, Gutub A. Applicable light-weight cryptography to secure medical data in IoT systems. J Res Eng Appl Sci. 2017;2(2):50–58. doi:10.46565/jreas.2017.v02i02.002
  • Alassaf N, Gutub A. Simulating light-weight-cryptography implementation for IoT healthcare data security applications. Int J E-Health Med Commun. 2010;10:1–15. doi:10.4018/IJEHMC.2019100101
  • Hureib E, Gutub A. Enhancing medical data security via combining elliptic curve cryptography and image steganography. Int J Comput Sci Netw Security (IJCSNS). 2020;20(8):1–8.
  • Shambour MK, Gutub A. Progress of IoT research technologies and applications serving Hajj and Umrah. Arab J Sci Eng. 2022;47:1253–1273. doi:10.1007/s13369-021-05838-7
  • Alassaf N, Gutub A, Parah SA, et al. Enhancing speed of SIMON: a light-weight-cryptographic algorithm for IoT applications. Multimed Tools Appl. 2019;78:32633–32657. doi:10.1007/s11042-018-6801-z
  • Roy PK, Saumya S, Singh JP, et al. Analysis of community question-answering issues via machine learning and deep learning: state-of-the-art review. CAAI Trans Intell Technol. 2023;8(1):95–117. doi:10.1049/cit2.12081
  • Jingfu. IOT security analysis of BDT-SVM multi-classification algorithm. Int J Comput Appl. 2020;45:170–179.
  • Yong-xiong Z, Liang-ming W, Lu-xia Y. A network attack discovery algorithm based on unbalanced sampling vehicle evolution strategy for intrusion detection. Int J Comput Appl. 2017;42(1):4–92.
  • Singh G, Khare N. A survey of intrusion detection from the perspective of intrusion datasets and machine learning techniques. Int J Comput Appl. 2021;44(7):659–669.
  • Thiyam B, Dey S. Efficient feature evaluation approach for a class-imbalanced dataset using machine learning. Procedia Comput Sci. 2023;218:2520–2532.
  • Thakkar A, Lohiya R. Attack classification using feature selection techniques: a comparative study. J Ambient Intell Humaniz Comput. 2021;22:1249–1266. doi:10.1007/s12652-020-02167-9
  • Park S-T, Li G, Hong J-C. A study on smart factory-based ambient intelligence context-aware intrusion detection system using machine learning. J Ambient Intell Humaniz Comput. 2018;11:1405–1412. doi:10.1007/s12652-018-0998-6
  • Yingshang L, Shouyu L, Wenchong F, et al. A hybrid feature selection algorithm combining information gain and genetic search for intrusion detection. J Phys Conf Ser. 2020;1601:1–10.
  • Bonab MS, Ghaffari A, Gharehchopogh FS, et al. A wrapper-based feature selection for improving performance of intrusion detection systems. Int J Commun Syst. 2020;33(12):1–26.
  • Almasoudy FH, Laftah AI-Yaseen W, Idrees A. Differential evolution wrapper feature selection for intrusion detection system. Procedia Comput Sci. 2019;167:1230–1239. doi:10.1016/j.procs.2020.03.438
  • Yonghao G, Li K, Guo Z, et al. Semi supervised K-means DDoS detection method using hybrid feature selection algorithm. IEEE Access. 2019;7:64351–64365. doi:10.1109/ACCESS.2019.2917532
  • Al-Yaseen WL, Idrees AK, Almasoudy FH. Wrapper feature selection method based differential evolution and extreme learning machine for intrusion detection system. Pattern Recognit. 2022;132:1–11.
  • Thaseen IS, Kumar CA, Ahmad A. Integrated intrusion detection model using Chi-square feature selection and ensemble of classifiers. Arab J Sci Eng. 2019;44:3357–3368. doi:10.1007/s13369-018-3507-5
  • Moustafa N, Slay J. A hybrid feature selection for network intrusion detection systems: central points. Australian Information Warfare & Security Conference; 2015, p. 1–10.
  • Hosseini S, Zade BMZ. New hybrid method for attack detection using combination of evolutionary algorithms, SVM, and ANN. Comput Netw. 2020;173:1–15.
  • Pham NT, Foo E, Suriadi S, et al. Improving performance of intrusion detection system using ensemble methods and feature selection. In Proceedings of the Australasian Computer Science Week Multiconference (ACSW ‘18). Association for Computing Machinery, 2018, p. 1–6.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.