TY - GEN
T1 - Multicodebook Neural Network Using Intelligent K-Means Clustering Based on Histogram Information for Multimodal Data Classification
AU - Marsum, M. Anwar
AU - Arsa, Dewa Made Sri
AU - Hermawan, Indra
AU - Jatmiko, Wisnu
AU - Nurhadiyatna, Adi
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/24
Y1 - 2018/9/24
N2 - Multimodal distribution makes classification more challenging. In some cases, it will cause a decrease in performance of the classifier. Therefore, we must select and utilize an appropriate classifier for this kind of data. In addition, multimodal distribution causes classification result of single-codebook-based classifiers fail to classify the data accurately. Some research has been conducted to build multicodebook-based classifier. In this study, we propose an enhancement of Multicodebook Fuzzy Neuro Generalized Learning Vector Quantization method using Intelligent K-Means clustering based on Histogram Information. First, We used Intelligent K-Means Clustering using histogram information as the number of cluster parameter. Next, We applied the clustering method to FNGLVQ in order to generate codebook candidate before the training process. The experiment is conducted using synthetic and benchmark datasets. Overall, the proposed method achieved a better accuracy compared to multi codebook FNGLVQ using IK-Means clustering based on anomalous pattern and original FNGLVQ with a margin of 4.14% and 17.29% respectively.
AB - Multimodal distribution makes classification more challenging. In some cases, it will cause a decrease in performance of the classifier. Therefore, we must select and utilize an appropriate classifier for this kind of data. In addition, multimodal distribution causes classification result of single-codebook-based classifiers fail to classify the data accurately. Some research has been conducted to build multicodebook-based classifier. In this study, we propose an enhancement of Multicodebook Fuzzy Neuro Generalized Learning Vector Quantization method using Intelligent K-Means clustering based on Histogram Information. First, We used Intelligent K-Means Clustering using histogram information as the number of cluster parameter. Next, We applied the clustering method to FNGLVQ in order to generate codebook candidate before the training process. The experiment is conducted using synthetic and benchmark datasets. Overall, the proposed method achieved a better accuracy compared to multi codebook FNGLVQ using IK-Means clustering based on anomalous pattern and original FNGLVQ with a margin of 4.14% and 17.29% respectively.
UR - http://www.scopus.com/inward/record.url?scp=85055507013&partnerID=8YFLogxK
U2 - 10.1109/IWBIS.2018.8471699
DO - 10.1109/IWBIS.2018.8471699
M3 - Conference contribution
AN - SCOPUS:85055507013
T3 - 2018 International Workshop on Big Data and Information Security, IWBIS 2018
SP - 129
EP - 135
BT - 2018 International Workshop on Big Data and Information Security, IWBIS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Workshop on Big Data and Information Security, IWBIS 2018
Y2 - 12 May 2018 through 13 May 2018
ER -