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دسته بندی:
سیستم های خبره - expert systems
سال انتشار:
2019
عنوان انگلیسی مقاله:
CWV-BANN-SVM ensemble learning classifier for an accurate diagnosis of breast cancer
ترجمه فارسی عنوان مقاله:
طبقه بندی یادگیری گروه CWV-BANN-SVM برای تشخیص دقیق سرطان پستان
منبع:
Sciencedirect - Elsevier - Measurement, 146 (2019) 557-570: doi:10:1016/j:measurement:2019:05:022
نویسنده:
Moloud Abdar ⇑, Vladimir Makarenkov
چکیده انگلیسی:
This paper presents a new data mining technique for an accurate prediction of breast cancer (BC), which
is one of the major mortality causes among women around the globe. The main objective of our study is
to expand an automatic expert system (ES) to provide an accurate diagnosis of BC. Both, Support Vector
Machines (SVMs) and Artificial Neural Networks (ANNs) were applied to analyze BC data. The wellknown
Wisconsin Breast Cancer Dataset (WBCD), available in the UCI repository, was examined in our
study. We first tested the SVM algorithm using various values of the C, e and c parameters. As a result
of the first experiment, we were able to observe that the adjustment of these regularization parameters
can greatly improve the performance of the traditional SVM algorithm applied for BC detection. The highest
obtained accuracy at the first step was 99.71%. Then, we performed a new BC detection approach
based on two ensemble learning techniques: the confidence-weighted voting method and the boosting
ensemble technique. Our model, called CWV-BANNSVM, combines boosting ANNs (BANN) and two
SVMs, using optimal parameters selected during the first experiment. The performance of the applied
methods was evaluated using several popular metrics, such as specificity, sensitivity, precision, FPR,
FNR, F1 score, AUC, Gini and accuracy. The proposed CWV-BANNSVM model was able to improve the performance
of the traditional machine learning algorithms applied to BC detection, reaching the accuracy of
100%. To overcome the overfitting issue, we determined and used some appropriate parameter values of
polynomial SVM. Our comparison with the existing studies dedicated to BC prediction suggests that the
proposed CWV-BANN-SVM model provides one of the best prediction performances overall.
Keywords: Data mining | Machine learning | Ensemble technique | Breast cancer | Support vector machine | Artificial neural network
قیمت: رایگان
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