TELKOMNIKA Telecommunication, Computing, Electronics and Control
The comparison study of kernel KC-means and support vector machines for classifying schizophrenia
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
The comparison study of kernel KC-means and support vector machines for classifying schizophrenia
The comparison study of kernel KC-means and support vector machines for classifying schizophrenia
Subject
Fast fuzzy clustering, KC-means, Kernel function, Schizophrenia classification, Support vector machines
Description
Schizophrenia is one of mental disorder that affects the mind, feeling,
and behavior. Its treatment is usually permanent and quite complicated;
therefore, early detection is important. Kernel KC-means and support vector machines are the methods known as a good classifier. This research, therefore, aims to compare kernel KC-means and support vector machines, using data obtained from Northwestern University, which consists of 171 schizophrenia and 221 non-schizophrenia samples. The performance accuracy, F1-score, and running time were examined using the 10-fold cross-validation method. From the experiments, kernel KC-means with the sixth-order polynomial kernel gives 87.18 percent accuracy and 93.15 percent F1-score at the faster running time than support vector machines. However, with the same kernel, it was further deduced from the results that support vector machines provides better performance with an accuracy of 88.78 percent and F1-score of 94.05 percent.
and behavior. Its treatment is usually permanent and quite complicated;
therefore, early detection is important. Kernel KC-means and support vector machines are the methods known as a good classifier. This research, therefore, aims to compare kernel KC-means and support vector machines, using data obtained from Northwestern University, which consists of 171 schizophrenia and 221 non-schizophrenia samples. The performance accuracy, F1-score, and running time were examined using the 10-fold cross-validation method. From the experiments, kernel KC-means with the sixth-order polynomial kernel gives 87.18 percent accuracy and 93.15 percent F1-score at the faster running time than support vector machines. However, with the same kernel, it was further deduced from the results that support vector machines provides better performance with an accuracy of 88.78 percent and F1-score of 94.05 percent.
Creator
Sri Hartini, Zuherman Rustam
Source
DOI: 10.12928/TELKOMNIKA.v18i3.14847
Publisher
Universitas Ahmad Dahlan
Date
June 2020
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Sri Hartini, Zuherman Rustam, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
The comparison study of kernel KC-means and support vector machines for classifying schizophrenia,” Repository Horizon University Indonesia, accessed April 12, 2025, https://repository.horizon.ac.id/items/show/3874.
The comparison study of kernel KC-means and support vector machines for classifying schizophrenia,” Repository Horizon University Indonesia, accessed April 12, 2025, https://repository.horizon.ac.id/items/show/3874.