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Correlation-based feature selection for association rule mining in semantic annotation of mammographic medical images

Mining of high dimension data for mammogram image classification is highly challenging. Feature reduction using subset selection plays enormous significance in the field of image mining to reduce the complexity of image mining process. This paper aims at investigating an improved image mining techni...

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Main Authors: Abubacker, Nirase Fathima, Azman, Azreen, C. Doraisamy, Shyamala, Azmi Murad, Masrah Azrifah, Elmanna, Mohamed Eltahir Makki, Saravanan, Rekha
Format: Conference or Workshop Item
Language:English
Published: Springer 2014
Online Access:http://psasir.upm.edu.my/id/eprint/39826/1/Correlation-based%20feature%20selection%20for%20association%20rule%20mining%20in%20semantic%20annotation%20of%20mammographic%20medical%20images.pdf
http://psasir.upm.edu.my/id/eprint/39826/
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spelling my.upm.eprints.398262016-07-28T08:45:45Z http://psasir.upm.edu.my/id/eprint/39826/ Correlation-based feature selection for association rule mining in semantic annotation of mammographic medical images Abubacker, Nirase Fathima Azman, Azreen C. Doraisamy, Shyamala Azmi Murad, Masrah Azrifah Elmanna, Mohamed Eltahir Makki Saravanan, Rekha Mining of high dimension data for mammogram image classification is highly challenging. Feature reduction using subset selection plays enormous significance in the field of image mining to reduce the complexity of image mining process. This paper aims at investigating an improved image mining technique to enhance the automatic and semi-automatic semantic image annotation of mammography images using multivariate filters, which is the Correlation-based Feature Selection (CFS). This feature selection method is then applied onto two association rules mining methods, the Apriori and a modified genetic association rule mining technique, the GARM, to classify mammography images into their pathological labels. The findings show that the classification accuracy is improved with the use of CFS in both Apriori and GARM mining techniques. Springer 2014 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/39826/1/Correlation-based%20feature%20selection%20for%20association%20rule%20mining%20in%20semantic%20annotation%20of%20mammographic%20medical%20images.pdf Abubacker, Nirase Fathima and Azman, Azreen and C. Doraisamy, Shyamala and Azmi Murad, Masrah Azrifah and Elmanna, Mohamed Eltahir Makki and Saravanan, Rekha (2014) Correlation-based feature selection for association rule mining in semantic annotation of mammographic medical images. In: 10th Asia Information Retrieval Societies Conference (AIRS 2014), 3-5 Dec. 2014, Kuching, Sarawak, Malaysia. (pp. 482-493). 10.1007/978-3-319-12844-3_41
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Mining of high dimension data for mammogram image classification is highly challenging. Feature reduction using subset selection plays enormous significance in the field of image mining to reduce the complexity of image mining process. This paper aims at investigating an improved image mining technique to enhance the automatic and semi-automatic semantic image annotation of mammography images using multivariate filters, which is the Correlation-based Feature Selection (CFS). This feature selection method is then applied onto two association rules mining methods, the Apriori and a modified genetic association rule mining technique, the GARM, to classify mammography images into their pathological labels. The findings show that the classification accuracy is improved with the use of CFS in both Apriori and GARM mining techniques.
format Conference or Workshop Item
author Abubacker, Nirase Fathima
Azman, Azreen
C. Doraisamy, Shyamala
Azmi Murad, Masrah Azrifah
Elmanna, Mohamed Eltahir Makki
Saravanan, Rekha
spellingShingle Abubacker, Nirase Fathima
Azman, Azreen
C. Doraisamy, Shyamala
Azmi Murad, Masrah Azrifah
Elmanna, Mohamed Eltahir Makki
Saravanan, Rekha
Correlation-based feature selection for association rule mining in semantic annotation of mammographic medical images
author_facet Abubacker, Nirase Fathima
Azman, Azreen
C. Doraisamy, Shyamala
Azmi Murad, Masrah Azrifah
Elmanna, Mohamed Eltahir Makki
Saravanan, Rekha
author_sort Abubacker, Nirase Fathima
title Correlation-based feature selection for association rule mining in semantic annotation of mammographic medical images
title_short Correlation-based feature selection for association rule mining in semantic annotation of mammographic medical images
title_full Correlation-based feature selection for association rule mining in semantic annotation of mammographic medical images
title_fullStr Correlation-based feature selection for association rule mining in semantic annotation of mammographic medical images
title_full_unstemmed Correlation-based feature selection for association rule mining in semantic annotation of mammographic medical images
title_sort correlation-based feature selection for association rule mining in semantic annotation of mammographic medical images
publisher Springer
publishDate 2014
url http://psasir.upm.edu.my/id/eprint/39826/1/Correlation-based%20feature%20selection%20for%20association%20rule%20mining%20in%20semantic%20annotation%20of%20mammographic%20medical%20images.pdf
http://psasir.upm.edu.my/id/eprint/39826/
_version_ 1643832536771592192
score 13.211869

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