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...
محفوظ في:
| المؤلفون الرئيسيون: | , |
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| التنسيق: | Theses and Dissertations NonPeerReviewed |
| منشور في: |
[Yogyakarta] : Universitas Gadjah Mada
2014
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://repository.ugm.ac.id/133398/ http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=74052 |
| الوسوم: |
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| المؤسسة: | Universitas Gadjah Mada |
| الملخص: | 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 |
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