Deep learning with ensemble classification method for sensor sampling decisions

Author(s):  
Sirine Taleb ◽  
Ahmad Al Sallab ◽  
Hazem Hajj ◽  
Zaher Dawy ◽  
Rahul Khanna ◽  
...  
2021 ◽  
Author(s):  
Wenfeng Li ◽  
Yuewu Yang ◽  
Liwei Zhang ◽  
Xiaochen Xu ◽  
Haobo Ma ◽  
...  

2019 ◽  
Vol 94 ◽  
pp. 524-535 ◽  
Author(s):  
Ningbo Liu ◽  
Yanan Xu ◽  
Yonghua Tian ◽  
Hongwei Ma ◽  
Shuliang Wen

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Jinyu Cong ◽  
Benzheng Wei ◽  
Yunlong He ◽  
Yilong Yin ◽  
Yuanjie Zheng

Breast cancer has been one of the main diseases that threatens women’s life. Early detection and diagnosis of breast cancer play an important role in reducing mortality of breast cancer. In this paper, we propose a selective ensemble method integrated with the KNN, SVM, and Naive Bayes to diagnose the breast cancer combining ultrasound images with mammography images. Our experimental results have shown that the selective classification method with an accuracy of 88.73% and sensitivity of 97.06% is efficient for breast cancer diagnosis. And indicator R presents a new way to choose the base classifier for ensemble learning.


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