Transforming big data into smart data: An insight on the use of the k‐nearest neighbors algorithm to obtain quality data
The k‐nearest neighbors algorithm is characterized as a simple yet effective data mining
technique. The main drawback of this technique appears when massive amounts of data …
technique. The main drawback of this technique appears when massive amounts of data …
Consumer credit risk assessment: A review from the state-of-the-art classification algorithms, data traits, and learning methods
Credit risk assessment is a crucial element in credit risk management. With the extensive
research on consumer credit risk assessment in recent decades, the abundance of literature …
research on consumer credit risk assessment in recent decades, the abundance of literature …
A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and …
Ensembles, especially ensembles of decision trees, are one of the most popular and
successful techniques in machine learning. Recently, the number of ensemble-based …
successful techniques in machine learning. Recently, the number of ensemble-based …
Image classification with deep learning in the presence of noisy labels: A survey
Image classification systems recently made a giant leap with the advancement of deep
neural networks. However, these systems require an excessive amount of labeled data to be …
neural networks. However, these systems require an excessive amount of labeled data to be …
Glenet: Boosting 3d object detectors with generative label uncertainty estimation
The inherent ambiguity in ground-truth annotations of 3D bounding boxes, caused by
occlusions, signal missing, or manual annotation errors, can confuse deep 3D object …
occlusions, signal missing, or manual annotation errors, can confuse deep 3D object …
Novel hybrid ensemble credit scoring model with stacking-based noise detection and weight assignment
J Yao, Z Wang, L Wang, M Liu, H Jiang… - Expert Systems with …, 2022 - Elsevier
Credit scoring is used to help financial institutions control default risks and reduce economic
losses, and a variety of mainstream machine learning and data mining algorithms have …
losses, and a variety of mainstream machine learning and data mining algorithms have …
Federated Learning with Instance-Dependent Noisy Label
Federated learning (FL) with noisy labels poses a significant challenge. Existing methods
designed for handling noisy labels in centralized learning tend to lose their effectiveness in …
designed for handling noisy labels in centralized learning tend to lose their effectiveness in …
Toward facial expression recognition in the wild via noise-tolerant network
Facial Expression Recognition (FER) has recently emerged as a crucial area in Human-
Computer Interaction (HCI) system for understanding the user's inner state and intention …
Computer Interaction (HCI) system for understanding the user's inner state and intention …
A balanced random learning strategy for CNN based Landsat image segmentation under imbalanced and noisy labels
X Zhao, Y Cheng, L Liang, H Wang, X Gao, J Wu - Pattern Recognition, 2023 - Elsevier
Landsat image segmentation is important for obtaining large-scale land cover maps. The
accuracy of CNN-based Landsat image segmentation highly depends on the quantity and …
accuracy of CNN-based Landsat image segmentation highly depends on the quantity and …
A label noise filtering method for regression based on adaptive threshold and noise score
C Li, Z Mao - Expert Systems with Applications, 2023 - Elsevier
The quality of training data plays a decisive role in the establishment of intelligent models.
Since raw data obtained from the real world are usually entwined with noise due to variety of …
Since raw data obtained from the real world are usually entwined with noise due to variety of …