TY - GEN
T1 - Feature selection and classification of breast cancer diagnosis based on support vector machines
AU - Purnami, Santi Wulan
AU - Rahayu, S. P.
AU - Embong, Abdullah
PY - 2008
Y1 - 2008
N2 - Support Vector Machines (SVM) is a new algorithm of data mining technique, recently received increasing popularity in machine learning community. This paper emphasizes how 1-norm SVM can be used in feature selection and smooth SVM (SSVM) for classification. As a case study, a breast cancer diagnosis was implemented. First, feature selection for support vector machines was utilized to determine the important features. Then, SSVM was used to classify the state of disease (benign or malignant) of breast cancer. As a result, SVM can achieve the state of the art performance on feature selection and classification.
AB - Support Vector Machines (SVM) is a new algorithm of data mining technique, recently received increasing popularity in machine learning community. This paper emphasizes how 1-norm SVM can be used in feature selection and smooth SVM (SSVM) for classification. As a case study, a breast cancer diagnosis was implemented. First, feature selection for support vector machines was utilized to determine the important features. Then, SSVM was used to classify the state of disease (benign or malignant) of breast cancer. As a result, SVM can achieve the state of the art performance on feature selection and classification.
UR - http://www.scopus.com/inward/record.url?scp=57349181767&partnerID=8YFLogxK
U2 - 10.1109/ITSIM.2008.4631603
DO - 10.1109/ITSIM.2008.4631603
M3 - Conference contribution
AN - SCOPUS:57349181767
SN - 9781424423286
T3 - Proceedings - International Symposium on Information Technology 2008, ITSim
BT - Proceedings - International Symposium on Information Technology 2008, ITSim
T2 - International Symposium on Information Technology 2008, ITSim
Y2 - 26 August 2008 through 29 August 2008
ER -