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...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Springer
2014
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| Online Access: | http://psasir.upm.edu.my/id/eprint/39826/ 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 |
| _version_ | 1848849252657135616 |
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| author | Abubacker, Nirase Fathima Azman, Azreen C. Doraisamy, Shyamala Azmi Murad, Masrah Azrifah Elmanna, Mohamed Eltahir Makki Saravanan, Rekha |
| 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 |
| building | UPM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T09:47:27Z |
| format | Conference or Workshop Item |
| id | upm-39826 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:47:27Z |
| publishDate | 2014 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-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 |
| 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 |
| title | 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_short | 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 |
| url | http://psasir.upm.edu.my/id/eprint/39826/ http://psasir.upm.edu.my/id/eprint/39826/ 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 |