Skip to main content

Analysis of Feature Selection Method for 3D Molecular Structure of Amphetamine-Type Stimulants (ATS) Drugs

  • Conference paper
  • First Online:
Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021) (SoCPaR 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 417))

Included in the following conference series:

  • 618 Accesses


This paper proposes an improved method to analyze the effectiveness of ATS drugs identification by using a few feature selection methods such as Sequential Forward Floating Selection (SFFS), Sequential Forward Selection (SFS), Sequential Backward Floating Selection (SBFS), Sequential Backward Selection (SBS) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE). The fundamental target of this paper is to compare which feature selection methods have better classification accuracy performance in identification for a large dataset. A comprehensive verification using WEKA is carried out to determine the performance of classification accuracy. This is achieved by comparing several classifiers with all features (without feature selection methods) and with selected features (with feature selection methods). From the experimental work, it was found that the performance of classification accuracy with selected features has similar accuracy if the performance accuracy done with all features. This shows that feature selection methods help to fasten and get better accuracy performance. The result also indicates that SFFS are the best feature selection methods to use to embed with SVM-RFE, while J48, IBk and Random Forest (RF) are the best three classifiers to use for future evaluation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions


  1. W. H. O. Geneva: Neuroscience of Psychoactive Substance Use and Dependence. World Health Organization, Switzerland (2004)

    Google Scholar 

  2. Ding, Y., Wilkins, D.: Improving the performance of SVM-RFE to select genes in microarray data. BMC Bioinform. 7(2), S12 (2006)

    Google Scholar 

  3. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)

    MATH  Google Scholar 

  4. Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1), 389–422 (2002)

    Article  Google Scholar 

  5. Hall, M.A.: Correlation-based feature subset selection for machine learning. Doctor of Philosophy Dissertation, University of Waikato, Hamilton, New Zealand (1999)

    Google Scholar 

  6. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks (1995)

    Google Scholar 

  7. Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1–2), 1–43 (1997)

    MATH  Google Scholar 

  8. Li, Z., Xie, W., Liu, T.: Efficient feature selection and classification for microarray data. PLoS ONE 13(8), e0202167 (2018)

    Google Scholar 

  9. Mundra, P.A., Rajapakse, J.C.: SVM-RFE with mrmr filter for gene selection. IEEE Trans. Nanobiosci. 9(1), 31–37 (2010)

    Article  Google Scholar 

  10. Portinale, L., Saitta, L.: Feature selection: state of the art. Feature selection, pp. 1–22. Universita del Piemonte Orientale, Alessandria (2002)

    Google Scholar 

  11. Pratama, S.F., Muda, A.K., Choo, Y.H., Muda, N.A.: A new swarm-based framework for handwritten authorship identification in forensic document analysis. In: Muda, A., Choo, Y.H., Abraham, A., N. Srihari, S. (eds.) Computational Intelligence in Digital Forensics: Forensic Investigation and Applications. SCI, vol. 555, pp. 385–411. Springer, Cham (2014).

  12. Pudil, P., Novovicova, J., Kittler, J.: Floating search methods in feature selection. Pattern Recognit. Lett. 15, 1119–1125 (1994)

    Google Scholar 

  13. Rustam, Z., Maghfirah, N.: Correlated based SVM-RFE as feature selection for cancer classification using microarray databases. In: AIP Conference Proceedings, vol. 2023, no. 1, p. 020235. AIP Publishing (2018)

    Google Scholar 

  14. Saeys, Y., Inza, I., Larranaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), pp. 2507–2517 (2007)

    Google Scholar 

  15. Sanz, H., Valim, C., Vegas, E., Oller, J.M., Reverter, F.: SVM-RFE: selection and visualization of the most relevant features through non-linear kernels. BMC Bioinform. 19(1), 432 (2018)

    Article  Google Scholar 

  16. Luhaniwal, V.R.: A comprehensive guide to feature selection using wrapper methods in Python. Analytics Vidhya, 24 October 2020.

  17. Tang, Y., Zhang, Y., Huang, Z.: Development of two-stage SVM-RFE gene selection strategy for microarray expression data analysis. IEEE/ACM Trans. Comput. Biol. Bioinform. 4(3), 365–381 (2007)

    Google Scholar 

  18. Ragan, A.: Medium. Medium, 11 October 2018.

  19. Yan, K., Zhang, D.: Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sens. Actuators B Chem. 212, 353–363 (2015)

    Article  Google Scholar 

  20. Yoon, S., Kim, S.: Mutual information-based SVM-RFE for diagnostic classification of digitized mammograms. Pattern Recogn. Lett. 30(16), 1489–1495 (2009)

    Article  Google Scholar 

  21. Zhang, Y., Deng, Q., Liang, W., Zou, X.: An efficient feature selection strategy based on multiple support vector machine technology with gene expression data. BioMed Res. Int. 2018 (2018)

    Google Scholar 

  22. Smith B.: An approach to graphs of linear forms (Unpublished work style) (unpublished)

    Google Scholar 

  23. Miller, E.H.: A note on reflector arrays (Periodical style—Accepted for publication). IEEE Trans. Antennas Propagat. (to be published)

    Google Scholar 

  24. Wang, J.: Fundamentals of erbium-doped fiber amplifiers arrays (periodical style—submitted for publication). IEEE J. Quantum Electron. (submitted for publication)

    Google Scholar 

  25. Bemister-Buffington, J., Wolf, A.J., Raschka, S., Kuhn, L.A.: machine learning to identify flexibility signatures of class A GPCR inhibition biomolecules 2020 10, 454 (2020).

  26. Xie, J., Lei, J., Xie, W., Gao, X., Shi, Y., Liu, X.: Novel hybrid feature selection algorithms for diagnosing erythemato-squamous diseases. In: He, J., Liu, X., Krupinski, E.A., Xu, G. (eds.) HIS 2012. LNCS, vol. 7231, pp. 173–185. Springer, Heidelberg (2012).

    Chapter  Google Scholar 

  27. Mohd, F., Noor, N.M.M.: A comparative study to evaluate filtering methods for crime data feature selection. Procedia Comput. Sci. 116, 113–120 (2017)

    Article  Google Scholar 

  28. Sequential feature selection - MATLAB & Simulink. (n.d.) MathWorks - Makers of MATLAB and Simulink - MATLAB & Simulink.

  29. Saw, Y.C., Muda, A.K., Yusoh, Z.I.M.: Significant features determination for ATS drug identification. J. Telecommun. Electron. Comput. Eng. (JTEC), 10(2–5), 87–92 (2018)

    Google Scholar 

  30. Saw, Y.C., Yusoh, Z.I.M., Muda, A.K., Abraham, A.: Ensemble filter-embedded feature ranking technique (FEFR) for 3D ATS drug molecular structure. Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 9, 124–134 (2017)

    Google Scholar 

  31. Minewiskan, T.S.: Feature selection (Data mining). Developer tools, technical documentation and coding examples | Microsoft Docs, 8 May 2018.

  32. De Niz, C., Rahman, R., Zhao, X., Pal, R.: Algorithms for drug sensitivity prediction. Algorithms 9(4), 77 (2016).

  33. Brownlee: An introduction to feature selection. Mach. Learn. Mastery (2014).

  34. Kaushik, M., Moores, A.: Nanocelluloses as versatile supports for metal nanoparticles and their applications in catalysis. Green Chem. 18(3), 622–637 (2016)

    Article  Google Scholar 

  35. Simple guide to confusion matrix terminology. Data School, 3 February 2020.

  36. Brownlee.: Hat is a confusion matrix in machine learning. Machine Learning Mastery, 18 November 18 2016.

  37. Witten, I.H., Frank, E, Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn., vol. 54, no. 2 (2011)

    Google Scholar 

  38. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  39. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, p. 680. Wiley, New York (2001)

    Google Scholar 

  40. Quinlan, J.R.: C 4.5: Programs for Machine Learning. Morgan Kaufmann Ser. Mach. Learn. (1993)

    Google Scholar 

Download references


The authors would like to acknowledge Universiti Teknikal Malaysia Melaka through the Fundamental Research Grant Scheme [FRGS/1/2020/FTMK-CACT/F00461] from the Ministry of Higher Education, Malaysia.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Azah Kamilah Muda .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Knight, P.E., Muda, A.K., Pratama, S.F. (2022). Analysis of Feature Selection Method for 3D Molecular Structure of Amphetamine-Type Stimulants (ATS) Drugs. In: Abraham, A., et al. Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021). SoCPaR 2021. Lecture Notes in Networks and Systems, vol 417. Springer, Cham.

Download citation

Publish with us

Policies and ethics