TY - GEN
T1 - Analysis of Feature Selection Method for 3D Molecular Structure of Amphetamine-Type Stimulants (ATS) Drugs
AU - Knight, Phoebe Ellyin
AU - Muda, Azah Kamilah
AU - Pratama, Satrya Fajri
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - 3D molecular structure
KW - Amphetamine-Type Stimulants (ATS)
KW - As Sequential Forward Floating Selection (SFFS)
KW - Drug image recognition
KW - Feature selection method
UR - http://www.scopus.com/inward/record.url?scp=85126244208&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-96302-6_11
DO - 10.1007/978-3-030-96302-6_11
M3 - Conference contribution
AN - SCOPUS:85126244208
SN - 9783030963019
T3 - Lecture Notes in Networks and Systems
SP - 118
EP - 135
BT - Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021)
A2 - Abraham, Ajith
A2 - Engelbrecht, Andries
A2 - Scotti, Fabio
A2 - Gandhi, Niketa
A2 - Manghirmalani Mishra, Pooja
A2 - Fortino, Giancarlo
A2 - Sakalauskas, Virgilijus
A2 - Pllana, Sabri
PB - Springer Nature Link
T2 - 13th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2021 and 13th World Congress on Nature and Biologically Inspired Computing, NaBIC 2021
Y2 - 15 December 2021 through 17 December 2021
ER -