TY - GEN
T1 - Enhance generalized learning vector quantization using unsupervised extreme learning machine and intelligent K-means clustering
AU - Ma'sum, M. Anwar
AU - Arsa, Dewa Made Sri
AU - Habibie, Novian
AU - Jatmiko, Wisnu
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - In this paper we proposed an enhancement of GLVQ classifier using USELM and IK-Means clustering. USELM is used to transform feature data into more separable form. The clustering method used to initiate codebook during training process. The proposed method has been tested using synthetic dataset and benchmark dataset. The proposed method has been compared to previous method and commonly used method. Experiment result shows that in over all dataset, the proposed method still has highest accuracy compared to others. Compared to GLVQ based classifier, the proposed method has better accuracy with margin 7.42%, 10.29%, 11.80%, and 8.11% for GLVQ, FNGLVQ, IK-Means-GLVQ, and USELM-GLVQ respectively. Compared to commonly used classifiers the proposed method has better accuracy with margin 1.94%, 2.93%, 11.61%, 31.37%, and 2.91% for MLP, Tree (J48), Linear-SVM, Sigmoid-SVM, and RBF-SVM respectively.
AB - In this paper we proposed an enhancement of GLVQ classifier using USELM and IK-Means clustering. USELM is used to transform feature data into more separable form. The clustering method used to initiate codebook during training process. The proposed method has been tested using synthetic dataset and benchmark dataset. The proposed method has been compared to previous method and commonly used method. Experiment result shows that in over all dataset, the proposed method still has highest accuracy compared to others. Compared to GLVQ based classifier, the proposed method has better accuracy with margin 7.42%, 10.29%, 11.80%, and 8.11% for GLVQ, FNGLVQ, IK-Means-GLVQ, and USELM-GLVQ respectively. Compared to commonly used classifiers the proposed method has better accuracy with margin 1.94%, 2.93%, 11.61%, 31.37%, and 2.91% for MLP, Tree (J48), Linear-SVM, Sigmoid-SVM, and RBF-SVM respectively.
KW - GLVQ
KW - IK-Means
KW - USELM
KW - enhancement
UR - http://www.scopus.com/inward/record.url?scp=85050765094&partnerID=8YFLogxK
U2 - 10.1109/IWBIS.2017.8275106
DO - 10.1109/IWBIS.2017.8275106
M3 - Conference contribution
AN - SCOPUS:85050765094
T3 - Proceedings - WBIS 2017: 2017 International Workshop on Big Data and Information Security
SP - 77
EP - 83
BT - Proceedings - WBIS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Workshop on Big Data and Information Security, WBIS 2017
Y2 - 23 September 2017 through 24 September 2017
ER -