Results 31 to 40 of about 106,507 (280)
Class Rectification Hard Mining for Imbalanced Deep Learning
, 2017 Recognising detailed facial or clothing attributes in images of people is a challenging task for computer vision, especially when the training data are both in very large scale and extremely imbalanced among different attribute classes.
Dong, Qi, Gong, Shaogang, Zhu, Xiatian
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Lightweight Micro-Expression Recognition on Composite Database
Applied Sciences, 2023 The potential of leveraging micro-expression in various areas such as security, health care and education has intensified interests in this area. Unlike facial expression, micro-expression is subtle and occurs rapidly, making it imperceptible.
Nur Aishah Ab Razak, Shahnorbanun Sahran
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Active Class Incremental Learning for Imbalanced Datasets [PDF]
, 2020 Accepted in IPCV workshop from ...
Eden Belouadah +3 more
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A Hybrid Sampling SVM Approach to Imbalanced Data Classification
Abstract and Applied Analysis, 2014 Imbalanced datasets are frequently found in many real applications. Resampling is one of the effective solutions due to generating a relatively balanced class distribution.
Qiang Wang
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A systematic study of the class imbalance problem in convolutional neural networks
, 2018 In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue.
Buda, Mateusz +2 more
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Superensemble classifier for improving predictions in imbalanced datasets [PDF]
Communications in Statistics: Case Studies, Data Analysis and Applications, 2020 Learning from an imbalanced dataset is a tricky proposition. Because these datasets are biased towards one class, most existing classifiers tend not to perform well on minority class examples. Conventional classifiers usually aim to optimize the overall accuracy without considering the relative distribution of each class.
Tanujit Chakraborty +1 more
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MEBoost: Mixing Estimators with Boosting for Imbalanced Data Classification
, 2017 Class imbalance problem has been a challenging research problem in the fields of machine learning and data mining as most real life datasets are imbalanced.
Ahmed, Sajid +6 more
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Posterior Re-calibration for Imbalanced Datasets
CoRR, 2020 Accepted to NeurIPS ...
Junjiao Tian +4 more
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Imbalanced SVM-Based Anomaly Detection Algorithm for Imbalanced Training Datasets [PDF]
ETRI Journal, 2017 Abnormal samples are usually difficult to obtain in production systems, resulting in imbalanced training sample sets. Namely, the number of positive samples is far less than the number of negative samples. Traditional Support Vector Machine (SVM)‐based anomaly detection algorithms perform poorly for highly imbalanced datasets: the learned ...
GuiPing Wang, JianXi Yang, Ren Li
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Oversampling for Imbalanced Learning Based on K-Means and SMOTE
, 2017 Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced class distributions. While different strategies exist to tackle this problem,
Bacao, Fernando +2 more
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