Open AccessComparison of Support Vector Machine Recursive Feature Elimination and Kernel Function as feature selection using Support Vector Machine for lung cancer classification
Author(s) -
Zuherman Rustam,
Selly Anastassia Amellia Kharis
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1442/1/012027
Subject(s) - support vector machine , feature selection , artificial intelligence , kernel (algebra) , pattern recognition (psychology) , computer science , feature (linguistics) , cancer , selection (genetic algorithm) , feature vector , machine learning , data mining , mathematics , medicine , linguistics , philosophy , combinatorics
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.