Results 21 to 30 of about 141,868 (281)
Active Learning for Imbalanced Ordinal Regression [PDF]
IEEE Access, 2020 Ordinal regression (OR), also called ordinal classification, is a special multi-classification designed for problems with ordered classes. Imbalanced data hinders the performance of classification algorithms, especially for OR algorithms, as imbalanced class distributions often arise in OR problems.
Jiaming Ge +4 more
openaire +2 more sources
Imbalanced Learning Based on Logistic Discrimination. [PDF]
Comput Intell Neurosci, 2016 In recent years, imbalanced learning problem has attracted more and more attentions from both academia and industry, and the problem is concerned with the performance of learning algorithms in the presence of data with severe class distribution skews. In this paper, we apply the well-known statistical model logistic discrimination to this problem and ...
Guo H, Zhi W, Liu H, Xu M.
europepmc +4 more sources
Optimizing imbalanced learning with genetic algorithm. [PDF]
Sci RepTraining AI models on imbalanced datasets with skewed class distributions poses a significant challenge, as it leads to model bias towards the majority class while neglecting the minority class. Various methods, such as Synthetic Minority Over Sampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), Generative Adversarial Networks (GANs) and ...
Safder MU +4 more
europepmc +4 more sources
Blending Query Strategy of Active Learning for Imbalanced Data
IEEE Access, 2022 When the data is imbalanced, often observed in the real-world, important minor class instances that are conducive to accurately predicting the decision boundary are less likely to be queried in the active learning for classification task.
Gwangsu Kim, Chang D. Yoo
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Toward a Balanced Feature Space for the Deep Imbalanced Regression
IEEE Access, 2023 Regression with imbalanced data has been regarded as a more realistic scenario due to the difficulty of data acquisition and label annotations. However, it has not been extensively studied compared to the imbalanced classification.
Jangho Lee
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Hellinger Distance Trees for Imbalanced Streams [PDF]
, 2014 Classifiers trained on data sets possessing an imbalanced class distribution are known to exhibit poor generalisation performance. This is known as the imbalanced learning problem.
Brooke, J. M. +3 more
core +2 more sources
Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets
IJAIN (International Journal of Advances in Intelligent Informatics), 2019 Imbalanced class data is a common issue faced in classification tasks. Deep Belief Networks (DBN) is a promising deep learning algorithm when learning from complex feature input.
A’inur A’fifah Amri +2 more
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A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets
Abstract and Applied Analysis, 2013 In imbalanced learning methods, resampling methods modify an imbalanced dataset to form a balanced dataset. Balanced data sets perform better than imbalanced datasets for many base classifiers.
Yong Zhang, Dapeng Wang
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Foundations of Computing and Decision Sciences, 2019 Sentiment classification is an important task which gained extensive attention both in academia and in industry. Many issues related to this task such as handling of negation or of sarcastic utterances were analyzed and accordingly addressed in previous ...
Lango Mateusz
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An Imbalanced R-STDP Learning Rule in Spiking Neural Networks for Medical Image Classification
IEEE Access, 2020 Spiking neural networks (SNNs) have the advantages of inherent power-efficiency, biological plausibility and good image recognition performance. They are good candidates for medical image classification especially when the labeled training data are ...
Qian Zhou, Cong Ren, Saibing Qi
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