Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy
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- 作者：Bartosz Krawczyka ; bartosz.krawczyk@pwr.edu.pl" class="auth_mail" title="E-mail the corresponding author ; Mikel Galarb ; mikel.galar@unavarra.es" class="auth_mail" title="E-mail the corresponding author ; Łukasz Jeleńc ; lukasz.jelen@pwr.edu.pl" class="auth_mail" title="E-mail the corresponding author ; Francisco Herrerad ; e ; herrera@decsai.ugr.es" class="auth_mail" title="E-mail the corresponding author
- 关键词：Machine Learning ; Classifier ensemble ; Imbalanced classification ; Evolutionary algorithms ; Clinical decision support ; Breast cancer
- 刊名：Applied Soft Computing
- 出版年：2016
- 出版时间：January 2016
- 年：2016
- 卷：38
- 期：Complete
- 页码：714-726
- 全文大小：2408 K
文摘
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Automatic clinical decision support system for breast cancer malignancy grading.
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Different methodologies for segmentation and feature extraction from FNA slides.
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An efficient classifier ensemble for imbalanced problems with difficult data.
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Ensemble combines boosting with evolutionary undersampling.
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Extensive computational experiments on a large database collected by authors.