KLASIFIKASI SVM (SUPPORT VECTOR MACHINE) DAN KOMBINASI SELEKSI FITUR PADA DIAGNOSIS KANKER PAYUDARA
of breast cancer has been widely implemented using machine learning. However, in medical data analysis, breast cancer diagnosis is usually faced with high dimensional features. The high dimensional features sometimes contain irrelevant features toward the classification process. Feature selection is...
| Main Authors: | , |
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| Format: | Thesis |
| Published: |
[Yogyakarta] : Universitas Gadjah Mada
2014
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| Subjects: |
| _version_ | 1826049015825825792 |
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| author | , Elvira Sukma Wahyuni , Noor Akhmad Setiawan, S.T., M.T., Ph.D. |
| author_facet | , Elvira Sukma Wahyuni , Noor Akhmad Setiawan, S.T., M.T., Ph.D. |
| author_sort | , Elvira Sukma Wahyuni |
| collection | UGM |
| description | of breast cancer has been widely implemented using machine
learning. However, in medical data analysis, breast cancer diagnosis is usually
faced with high dimensional features. The high dimensional features sometimes
contain irrelevant features toward the classification process. Feature selection is a
method to eliminate irrelevant features. It can improve the performance of
diagnosis. The objective of this research is to develop a feature selection method
for breast cancer diagnosis based on rough set and F-score combination.
Performance of combination features selection was applied in Wisconsin
Breast Cancer Dataset (WBCD). F-score feature selection method and Rough set
are combined subsequently by applied Rough set firstly. Than the result of
reduced subset feature by Rough set will be selected with F-score feature selection
method. Improvement the performance of diagnosis would be evaluated based on
the average of sensitivity, ROC AUC, accuracy, and running time with 100 times
experiment. Furthermore, the results would be compared with the performance of
feature selection method when it is applied individually and simultaneously.
The result shows that the combination of F-score feature selection method
and rough set achieves the optimal feature and superior performance compared
with F-score and Rough set when applied individually. The obtained of sensitivity
0.9714, ROC AUC 0.9700, accuracy 97.05%, and the running time 0.0722 s.
Keywords : Feature selection, Rough set, F-score, SVM classification, Breast
cancer diagnosis |
| first_indexed | 2024-03-13T23:39:10Z |
| format | Thesis |
| id | oai:generic.eprints.org:133398 |
| institution | Universiti Gadjah Mada |
| last_indexed | 2024-03-13T23:39:10Z |
| publishDate | 2014 |
| publisher | [Yogyakarta] : Universitas Gadjah Mada |
| record_format | dspace |
| spelling | oai:generic.eprints.org:1333982016-03-04T07:55:47Z https://repository.ugm.ac.id/133398/ KLASIFIKASI SVM (SUPPORT VECTOR MACHINE) DAN KOMBINASI SELEKSI FITUR PADA DIAGNOSIS KANKER PAYUDARA , Elvira Sukma Wahyuni , Noor Akhmad Setiawan, S.T., M.T., Ph.D. ETD of breast cancer has been widely implemented using machine learning. However, in medical data analysis, breast cancer diagnosis is usually faced with high dimensional features. The high dimensional features sometimes contain irrelevant features toward the classification process. Feature selection is a method to eliminate irrelevant features. It can improve the performance of diagnosis. The objective of this research is to develop a feature selection method for breast cancer diagnosis based on rough set and F-score combination. Performance of combination features selection was applied in Wisconsin Breast Cancer Dataset (WBCD). F-score feature selection method and Rough set are combined subsequently by applied Rough set firstly. Than the result of reduced subset feature by Rough set will be selected with F-score feature selection method. Improvement the performance of diagnosis would be evaluated based on the average of sensitivity, ROC AUC, accuracy, and running time with 100 times experiment. Furthermore, the results would be compared with the performance of feature selection method when it is applied individually and simultaneously. The result shows that the combination of F-score feature selection method and rough set achieves the optimal feature and superior performance compared with F-score and Rough set when applied individually. The obtained of sensitivity 0.9714, ROC AUC 0.9700, accuracy 97.05%, and the running time 0.0722 s. Keywords : Feature selection, Rough set, F-score, SVM classification, Breast cancer diagnosis [Yogyakarta] : Universitas Gadjah Mada 2014 Thesis NonPeerReviewed , Elvira Sukma Wahyuni and , Noor Akhmad Setiawan, S.T., M.T., Ph.D. (2014) KLASIFIKASI SVM (SUPPORT VECTOR MACHINE) DAN KOMBINASI SELEKSI FITUR PADA DIAGNOSIS KANKER PAYUDARA. UNSPECIFIED thesis, UNSPECIFIED. http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=74052 |
| spellingShingle | ETD , Elvira Sukma Wahyuni , Noor Akhmad Setiawan, S.T., M.T., Ph.D. KLASIFIKASI SVM (SUPPORT VECTOR MACHINE) DAN KOMBINASI SELEKSI FITUR PADA DIAGNOSIS KANKER PAYUDARA |
| title | KLASIFIKASI SVM (SUPPORT VECTOR MACHINE) DAN KOMBINASI SELEKSI FITUR PADA DIAGNOSIS KANKER PAYUDARA |
| title_full | KLASIFIKASI SVM (SUPPORT VECTOR MACHINE) DAN KOMBINASI SELEKSI FITUR PADA DIAGNOSIS KANKER PAYUDARA |
| title_fullStr | KLASIFIKASI SVM (SUPPORT VECTOR MACHINE) DAN KOMBINASI SELEKSI FITUR PADA DIAGNOSIS KANKER PAYUDARA |
| title_full_unstemmed | KLASIFIKASI SVM (SUPPORT VECTOR MACHINE) DAN KOMBINASI SELEKSI FITUR PADA DIAGNOSIS KANKER PAYUDARA |
| title_short | KLASIFIKASI SVM (SUPPORT VECTOR MACHINE) DAN KOMBINASI SELEKSI FITUR PADA DIAGNOSIS KANKER PAYUDARA |
| title_sort | klasifikasi svm support vector machine dan kombinasi seleksi fitur pada diagnosis kanker payudara |
| topic | ETD |
| work_keys_str_mv | AT elvirasukmawahyuni klasifikasisvmsupportvectormachinedankombinasiseleksifiturpadadiagnosiskankerpayudara AT noorakhmadsetiawanstmtphd klasifikasisvmsupportvectormachinedankombinasiseleksifiturpadadiagnosiskankerpayudara |