TY  - JOUR
T1  - Selection of Tuning Parameter in L1-Support Vector Machine via.
Particle Swarm Optimization Method
AU - Saber Qasim, Omar AU - Abdulmunim Al-Thanoon, Niam AU - Yahya Algamal, Zakariya 
JO  - Journal of Engineering and Applied Sciences
VL  - 15
IS  - 1
SP  - 310
EP  - 318
PY  - 2020
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2020.310.318
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2020.310.318
KW  - QSAR
KW  -L1-norm
KW  -classification
KW  -penalized support vector machine
KW  -particle swarm optimization
KW  -cluster
AB  - Descriptor selection for classification methods is one of the most important topics in the
chemometrics. The selection of descriptors can be considered to be a variable selection problem that aims to
find a small subset of descriptors that has the most discriminative information for the classification target.
Penalized Support Vector Machine (PSVM) is one of the most effective embedded methods and it is more
preferable than the Support Vector Machine (SVM) because PSVM combines the standard SVM with a penalty
to simultaneously perform both variable selection and classification. The PSVM with L1-norm is the most
widely used methods. However, the efficiency of PSVM with L1-norm depends on appropriately choosing the
tuning parameter which is involved in the L1-norm penalty. In this study, a particle swarm optimization method
which is a metaheuristic continuous algorithm is proposed to determine the tuning parameter in PSVM with
L1-norm penalty. The proposed method will efficiently help to find the most significant descriptors in
constructing Quantitative Structure–Activity Relationship classification (QSAR) model with high classification
performance. Depend on the four datasets, the experimental results show the favorable performance of the
proposed method when the number of descriptors is high and the sample size is low comparing with other
competitor methods.
ER  -