Results 281 to 290 of about 4,590,312 (339)

On the joint-effect of class imbalance and overlap: a critical review

Artificial Intelligence Review, 2022
Miriam Seoane Santos, Pedro Henriques Abreu, Nathalie Japkowicz   +2 more
exaly   +2 more sources

Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance

Renewable & Sustainable Energy Reviews, 2023
Most existing data-driven power system short-term voltage stability assessment (STVSA) approaches presume class-balanced input data. However, in practical applications, the occurrence of short-term voltage instability following a disturbance is minimal ...
Yang Li, Jiting Cao, Yan Xu, Lipeng Zhu, Z. Dong   +4 more
semanticscholar   +1 more source

On the Class Imbalance Problem

2008 Fourth International Conference on Natural Computation, 2008
The class imbalance problem has been recognized in many practical domains and a hot topic of machine learning in recent years. In such a problem, almost all the examples are labeled as one class, while far fewer examples are labeled as the other class, usually the more important class.
Xinjian Guo, Yilong Yin, Cailing Dong 0003, Gongping Yang 0001, Guang-Tong Zhou   +4 more
openaire   +1 more source

Class Imbalance, Redux

2011 IEEE 11th International Conference on Data Mining, 2011
Class imbalance (i.e., scenarios in which classes are unequally represented in the training data) occurs in many real-world learning tasks. Yet despite its practical importance, there is no established theory of class imbalance, and existing methods for handling it are therefore not well motivated.
Byron C. Wallace, Kevin Small, Carla E. Brodley, Thomas A. Trikalinos   +3 more
openaire   +1 more source

Automated Endoscopic Image Classification via Deep Neural Network With Class Imbalance Loss

IEEE Transactions on Instrumentation and Measurement, 2023
Recently, many computer-aided diagnosis (CAD) methods have been proposed to help physicians automatically classify endoscopic images. However, most existing methods often result in poor performance, especially for the minority classes, when the dataset ...
Guanghui Yue, Peishan Wei, Yun Liu, Yu Luo, Jingfeng Du, Tianfu Wang   +5 more
semanticscholar   +1 more source

Class-Imbalance Adversarial Transfer Learning Network for Cross-Domain Fault Diagnosis With Imbalanced Data

IEEE Transactions on Instrumentation and Measurement, 2022
Recently, cross-domain fault diagnosis based on transfer learning methods has been extensively explored and well-addressed when class-balance data with supervision information are available.
Jiachen Kuang, Guanghua Xu, T. Tao, Qingqiang Wu   +3 more
semanticscholar   +1 more source

Federated Learning for Automatic Modulation Classification Under Class Imbalance and Varying Noise Condition

IEEE Transactions on Cognitive Communications and Networking, 2022
Automatic modulation classification (AMC) is a promising technology for identifying modulation types, and deep learning (DL)-based AMC is one of its main research directions.
Yu Wang, Guan Gui, H. Gačanin, B. Adebisi, H. Sari, F. Adachi   +5 more
semanticscholar   +1 more source

The CURE for Class Imbalance

2019
Addressing the class imbalance problem is critical for several real world applications. The application of pre-processing methods is a popular way of dealing with this problem. These solutions increase the rare class examples and/or decrease the normal class cases.
Colin Bellinger, Paula Branco, Luís Torgo   +2 more
openaire   +2 more sources

Exploratory Under-Sampling for Class-Imbalance Learning

Sixth International Conference on Data Mining (ICDM'06), 2006
Undersampling is a popular method in dealing with class-imbalance problems, which uses only a subset of the majority class and thus is very efficient. The main deficiency is that many majority class examples are ignored. We propose two algorithms to overcome this deficiency. EasyEnsemble samples several subsets from the majority class, trains a learner
Xu-Ying Liu, Jianxin Wu 0001, Zhi-Hua Zhou   +2 more
openaire   +2 more sources

SMOTE vs. SMOTEENN: A Study on the Performance of Resampling Algorithms for Addressing Class Imbalance in Regression Models

Algorithms
Class imbalance is a prevalent challenge in machine learning that arises from skewed data distributions in one class over another, causing models to prioritize the majority class and underperform on the minority classes.
G. Husain, D. Nasef, Rejath Jose, Jonathan Mayer, M. Bekbolatova, Timothy Devine, Milan Toma   +6 more
semanticscholar   +1 more source