An ensemble classifier learning approach to ROC optimization

Author(s):  
Sheng Gao ◽  
Chin-Hui Lee ◽  
Joo Hwee Lim

2018 ◽  
Vol 22 (5) ◽  
pp. 762-777 ◽  
Author(s):  
Md Asafuddoula ◽  
Brijesh Verma ◽  
Mengjie Zhang




2021 ◽  
Author(s):  
Carlos Manuel Viriato Neto ◽  
Luca Garcia Honorio ◽  
Eduardo Aguiar

This paper focuses on the new model of classification of wagon bogie springs condition through images acquired by a wayside equipment. As such, we are discussing the application of a deep rule-based (DRB) classifier learning approach to achieve ahigh classification of a bogie, and check if they either have spring problems or not. We use a pre-trained VGG19 deep convolutional neural network to extract the attributes from images to be used as input to the classifiers. The performance is calculated based on the data set composed of images provided by a Brazilian railway company. The presented results of the report demonstrate the relative performance of applying the DRB classifier to the questions raised.



2020 ◽  
Vol 79 (33-34) ◽  
pp. 24463-24486
Author(s):  
Navid Danapur ◽  
Sakineh Asghari Aghjeh Dizaj ◽  
Vahid Rostami


2018 ◽  
Vol 313 ◽  
pp. 135-142 ◽  
Author(s):  
Samya Amiri ◽  
Mohamed Ali Mahjoub ◽  
Islem Rekik




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