Comparison of machine learning classifiers for dimensionally reduced fMRI data using random projection and principal component analysis
Machine learning has opened up the opportunity for understanding how the brain works. In this paper, functional magnetic resonance imaging (fMRI) data are analyzed with reduced dimension.We have carried out a performance comparison of random projection (RP) and principal component analysis (PCA) wi...
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2019
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| Online Access: | http://irep.iium.edu.my/78086/ http://irep.iium.edu.my/78086/1/ICOM_2019_paper_5.pdf http://irep.iium.edu.my/78086/7/78086_acceptance.pdf |
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iium-780862020-01-28T05:22:58Z http://irep.iium.edu.my/78086/ Comparison of machine learning classifiers for dimensionally reduced fMRI data using random projection and principal component analysis Htike, Zaw Zaw Mohd Suhaimi, Nur Farahana T Technology (General) Machine learning has opened up the opportunity for understanding how the brain works. In this paper, functional magnetic resonance imaging (fMRI) data are analyzed with reduced dimension.We have carried out a performance comparison of random projection (RP) and principal component analysis (PCA) with different number of components of fMRI data. In addition to that, six different types of machine learning algorithm have been used. In particular, the Haxby dataset is chosen for our experiment. The dataset comprises 9 classes for object recognition. 10-fold cross validation step has been employed. We have discovered that RP outperforms PCA when the former is paired with logistic regression, Gaussian Naive Bayes and linear support vector machine. The best pair for this study was found to be PCA and k-nearest neighbors. Nevertheless, each algorithm was found to have its own strengths for fMRI classification approach. 2019-10 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/78086/1/ICOM_2019_paper_5.pdf application/pdf en http://irep.iium.edu.my/78086/7/78086_acceptance.pdf Htike, Zaw Zaw and Mohd Suhaimi, Nur Farahana (2019) Comparison of machine learning classifiers for dimensionally reduced fMRI data using random projection and principal component analysis. In: International Conference on Mechatronics, 30-31 Oct 2019, Putrajaya. (In Press) |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
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Online Access |
| language |
English English |
| topic |
T Technology (General) |
| spellingShingle |
T Technology (General) Htike, Zaw Zaw Mohd Suhaimi, Nur Farahana Comparison of machine learning classifiers for dimensionally reduced fMRI data using random projection and principal component analysis |
| description |
Machine learning has opened up the opportunity
for understanding how the brain works. In this paper, functional magnetic resonance imaging (fMRI) data are analyzed with reduced dimension.We have carried out a performance comparison of random projection (RP) and principal component analysis (PCA) with different number of components of fMRI data. In addition to that, six different types of machine learning algorithm have been used. In particular, the Haxby dataset is chosen for our experiment. The dataset comprises 9 classes for object recognition. 10-fold cross validation step has been employed. We have discovered that RP outperforms PCA when the former is paired with logistic regression, Gaussian Naive Bayes and linear support vector machine. The best pair for this study was found
to be PCA and k-nearest neighbors. Nevertheless, each algorithm was found to have its own strengths for fMRI classification approach. |
| format |
Conference or Workshop Item |
| author |
Htike, Zaw Zaw Mohd Suhaimi, Nur Farahana |
| author_facet |
Htike, Zaw Zaw Mohd Suhaimi, Nur Farahana |
| author_sort |
Htike, Zaw Zaw |
| title |
Comparison of machine learning classifiers for dimensionally reduced fMRI data using random projection and principal component analysis |
| title_short |
Comparison of machine learning classifiers for dimensionally reduced fMRI data using random projection and principal component analysis |
| title_full |
Comparison of machine learning classifiers for dimensionally reduced fMRI data using random projection and principal component analysis |
| title_fullStr |
Comparison of machine learning classifiers for dimensionally reduced fMRI data using random projection and principal component analysis |
| title_full_unstemmed |
Comparison of machine learning classifiers for dimensionally reduced fMRI data using random projection and principal component analysis |
| title_sort |
comparison of machine learning classifiers for dimensionally reduced fmri data using random projection and principal component analysis |
| publishDate |
2019 |
| url |
http://irep.iium.edu.my/78086/ http://irep.iium.edu.my/78086/1/ICOM_2019_paper_5.pdf http://irep.iium.edu.my/78086/7/78086_acceptance.pdf |
| first_indexed |
2023-09-18T21:50:03Z |
| last_indexed |
2023-09-18T21:50:03Z |
| _version_ |
1777413708748685312 |