TY - GEN
T1 - SMOTE-least square support vector machine for classification of multiclass imbalanced data
AU - Purnami, Santi Wulan
AU - Trapsilasiwi, Rani Kemala
N1 - Publisher Copyright:
© 2017 ACM.
PY - 2017/2/24
Y1 - 2017/2/24
N2 - Dealing with multiclass classification problem is still considered as significant hurdle to determine an efficient classifier. Moreover, this task is getting rough when it comes to imbalanced data, which defined as the number of some classes are much bigger than the others. This condition could cause the classifier tends to predict the majority class and ignore the minority class. This study proposed Synthetic Minority Oversampling Technique-Least Square Support Vector Machine (SMOTE-LSSVM) to build a classifier addressing this problem. Particle Swarm Optimization-Gravitational Search Algorithm (PSO-GSA) was used to optimize the parameters of LS-SVM, while SMOTE was employed to balance the data. The effectiveness of SMOTE-LSSVM was examined on malignancy of breast cancer dataset. Results of this studies showed that the accuracy rate after applying SMOTE increased significantly compare to the results without applying SMOTE.
AB - Dealing with multiclass classification problem is still considered as significant hurdle to determine an efficient classifier. Moreover, this task is getting rough when it comes to imbalanced data, which defined as the number of some classes are much bigger than the others. This condition could cause the classifier tends to predict the majority class and ignore the minority class. This study proposed Synthetic Minority Oversampling Technique-Least Square Support Vector Machine (SMOTE-LSSVM) to build a classifier addressing this problem. Particle Swarm Optimization-Gravitational Search Algorithm (PSO-GSA) was used to optimize the parameters of LS-SVM, while SMOTE was employed to balance the data. The effectiveness of SMOTE-LSSVM was examined on malignancy of breast cancer dataset. Results of this studies showed that the accuracy rate after applying SMOTE increased significantly compare to the results without applying SMOTE.
KW - Imbalanced data
KW - Least square support vector machine
KW - Multiclass
KW - Particle swarm optimization-gravitational search algorithm
KW - Synthetic minority oversampling technique
UR - http://www.scopus.com/inward/record.url?scp=85024379718&partnerID=8YFLogxK
U2 - 10.1145/3055635.3056581
DO - 10.1145/3055635.3056581
M3 - Conference contribution
AN - SCOPUS:85024379718
T3 - ACM International Conference Proceeding Series
SP - 107
EP - 111
BT - Proceedings of 2017 9th International Conference on Machine Learning and Computing, ICMLC 2017
PB - Association for Computing Machinery
T2 - 9th International Conference on Machine Learning and Computing, ICMLC 2017
Y2 - 24 February 2017 through 26 February 2017
ER -