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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| Format: | Proceeding Paper |
| Language: | English English |
| Published: |
IEEE
2019
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| Online Access: | http://irep.iium.edu.my/78086/ http://irep.iium.edu.my/78086/13/78086_Comparison%20of%20Machine%20Learning%20Classifiers%20for%20dimensionally%20reduced%20fMRI%20data.pdf http://irep.iium.edu.my/78086/14/78086_Comparison%20of%20Machine%20Learning%20Classifiers%20for%20dimensionally%20reduced%20fMRI%20data_SCOPUS.pdf |
| _version_ | 1848788550291554304 |
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| author | Mohd Suhaimi, Nur Farahana Htike, Zaw Zaw |
| author_facet | Mohd Suhaimi, Nur Farahana Htike, Zaw Zaw |
| author_sort | Mohd Suhaimi, Nur Farahana |
| building | IIUM Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T17:42:36Z |
| format | Proceeding Paper |
| id | iium-78086 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T17:42:36Z |
| publishDate | 2019 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-780862020-06-02T17:49:56Z http://irep.iium.edu.my/78086/ Comparison of machine learning classifiers for dimensionally reduced fMRI data using random projection and principal component analysis Mohd Suhaimi, Nur Farahana Htike, Zaw Zaw 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. IEEE 2019-10 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/78086/13/78086_Comparison%20of%20Machine%20Learning%20Classifiers%20for%20dimensionally%20reduced%20fMRI%20data.pdf application/pdf en http://irep.iium.edu.my/78086/14/78086_Comparison%20of%20Machine%20Learning%20Classifiers%20for%20dimensionally%20reduced%20fMRI%20data_SCOPUS.pdf Mohd Suhaimi, Nur Farahana and Htike, Zaw Zaw (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. |
| spellingShingle | T Technology (General) Mohd Suhaimi, Nur Farahana Htike, Zaw Zaw Comparison of machine learning classifiers for dimensionally reduced fMRI data using random projection and principal component analysis |
| title | 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_short | 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 |
| topic | T Technology (General) |
| url | http://irep.iium.edu.my/78086/ http://irep.iium.edu.my/78086/13/78086_Comparison%20of%20Machine%20Learning%20Classifiers%20for%20dimensionally%20reduced%20fMRI%20data.pdf http://irep.iium.edu.my/78086/14/78086_Comparison%20of%20Machine%20Learning%20Classifiers%20for%20dimensionally%20reduced%20fMRI%20data_SCOPUS.pdf |