Classification in the presence of label noise: a survey
B Frénay, M Verleysen - IEEE transactions on neural networks …, 2013 - ieeexplore.ieee.org
Label noise is an important issue in classification, with many potential negative
consequences. For example, the accuracy of predictions may decrease, whereas the …
consequences. For example, the accuracy of predictions may decrease, whereas the …
Rolling bearing fault diagnosis using an optimization deep belief network
H Shao, H Jiang, X Zhang, M Niu - Measurement Science and …, 2015 - iopscience.iop.org
The vibration signals measured from a rolling bearing are usually affected by the variable
operating conditions and background noise which lead to the diversity and complexity of the …
operating conditions and background noise which lead to the diversity and complexity of the …
Hyperspectral image classification in the presence of noisy labels
Label information plays an important role in a supervised hyperspectral image classification
problem. However, current classification methods all ignore an important and inevitable …
problem. However, current classification methods all ignore an important and inevitable …
A random forest classifier for lymph diseases
AT Azar, HI Elshazly, AE Hassanien… - Computer methods and …, 2014 - Elsevier
Abstract Machine learning-based classification techniques provide support for the decision-
making process in many areas of health care, including diagnosis, prognosis, screening, etc …
making process in many areas of health care, including diagnosis, prognosis, screening, etc …
Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring
Previous studies about ensembles of classifiers for bankruptcy prediction and credit scoring
have been presented. In these studies, different ensemble schemes for complex classifiers …
have been presented. In these studies, different ensemble schemes for complex classifiers …
Credal-C4. 5: Decision tree based on imprecise probabilities to classify noisy data
In the area of classification, C4. 5 is a known algorithm widely used to design decision trees.
In this algorithm, a pruning process is carried out to solve the problem of the over-fitting. A …
In this algorithm, a pruning process is carried out to solve the problem of the over-fitting. A …
Multilayer spectral–spatial graphs for label noisy robust hyperspectral image classification
In hyperspectral image (HSI) analysis, label information is a scarce resource and it is
unavoidably affected by human and nonhuman factors, resulting in a large amount of label …
unavoidably affected by human and nonhuman factors, resulting in a large amount of label …
A comparison of random forest based algorithms: random credal random forest versus oblique random forest
Random forest (RF) is an ensemble learning method, and it is considered a reference due to
its excellent performance. Several improvements in RF have been published. A kind of …
its excellent performance. Several improvements in RF have been published. A kind of …
Enhancing accuracy and interpretability of ensemble strategies in credit risk assessment. A correlated-adjusted decision forest proposal
R Florez-Lopez, JM Ramon-Jeronimo - Expert Systems with Applications, 2015 - Elsevier
Credit risk assessment is a critical topic for finance activity and bankruptcy prediction that
has been broadly explored using statistical models and Machine Learning methods …
has been broadly explored using statistical models and Machine Learning methods …
DECODE: Deep confidence network for robust image classification
Recent years have witnessed the success of deep convolutional neural networks for image
classification and many related tasks. It should be pointed out that the existing training …
classification and many related tasks. It should be pointed out that the existing training …