TY - JOUR
T1 - Comparison of Support Vector Machine Recursive Feature Elimination and Kernel Function as feature selection using Support Vector Machine for lung cancer classification
AU - Rustam, Z.
AU - Kharis, S. A.A.
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2020/1/29
Y1 - 2020/1/29
N2 - Cancer is the uncontrolled growth of abnormal cell that need a proper treatment. Cancer is second leading cause of death according to the World Health Organization in 2018. There are more than 120 types of cancer, one of them is lung cancer. Cancer classification has been able to maximize diagnosis, treatment, and management of cancer. Many studies have examined the classification of cancer using microarrays data. Microarray data consists of thousands of features (genes) but only has dozens or hundreds of samples. This can reduce the accuracy of classification so that the selection of features is needed before the classification process. In this research, the feature selection methods are Support Vector Machine Recursive Feature Elimination (SVM-RFE) and Kernel Function and the classification method is Support Vector Machine (SVM). The results showed SVM using SVM-RFE as feature selection is better than SVM method without using feature selection and Gaussian Kernel Function.
AB - Cancer is the uncontrolled growth of abnormal cell that need a proper treatment. Cancer is second leading cause of death according to the World Health Organization in 2018. There are more than 120 types of cancer, one of them is lung cancer. Cancer classification has been able to maximize diagnosis, treatment, and management of cancer. Many studies have examined the classification of cancer using microarrays data. Microarray data consists of thousands of features (genes) but only has dozens or hundreds of samples. This can reduce the accuracy of classification so that the selection of features is needed before the classification process. In this research, the feature selection methods are Support Vector Machine Recursive Feature Elimination (SVM-RFE) and Kernel Function and the classification method is Support Vector Machine (SVM). The results showed SVM using SVM-RFE as feature selection is better than SVM method without using feature selection and Gaussian Kernel Function.
KW - Kernel function
KW - lung cancer
KW - recursive feature elimination
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85079665612&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1442/1/012027
DO - 10.1088/1742-6596/1442/1/012027
M3 - Conference article
AN - SCOPUS:85079665612
SN - 1742-6588
VL - 1442
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012027
T2 - Basic and Applied Sciences Interdisciplinary Conference 2017, BASIC 2017
Y2 - 18 August 2017 through 19 August 2017
ER -