Results 11 to 20 of about 98,421 (281)
Dialog Speech Sentiment Classification for Imbalanced Datasets [PDF]
, 2021 Speech is the most common way humans express their feel- ings, and sentiment analysis is the use of tools such as natural language processing and computational algorithms to identify the polarity of these feelings. Even though this field has seen tremendous advancements in the last two decades, the task of effectively detecting under represented sen ...
Nicolaou, Sergis +6 more
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LoRAS: an oversampling approach for imbalanced datasets [PDF]
Machine Learning, 2020 AbstractThe Synthetic Minority Oversampling TEchnique (SMOTE) is widely-used for the analysis of imbalanced datasets. It is known that SMOTE frequently over-generalizes the minority class, leading to misclassifications for the majority class, and effecting the overall balance of the model.
Saptarshi Bej +4 more
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Learning Imbalanced Datasets With Maximum Margin Loss
2021 IEEE International Conference on Image Processing (ICIP), 2021 A learning algorithm referred to as Maximum Margin (MM) is proposed for considering the class-imbalance data learning issue: the trained model tends to predict the majority of classes rather than the minority ones. That is, underfitting for minority classes seems to be one of the challenges of generalization.
Kang, Haeyong, Vu, Thang, Yoo, Chang D.
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Active Learning for Imbalanced Datasets
2020 IEEE Winter Conference on Applications of Computer Vision (WACV), 2020 Active learning increases the effectiveness of labeling when only subsets of unlabeled datasets can be processed manually. To our knowledge, existing algorithms are designed under the assumption that datasets are balanced. However, many real-life datasets are actually imbalanced and we propose two adaptations of active learning to tackle imbalance ...
Aggarwal, Umang +2 more
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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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Over-sampling imbalanced datasets using the Covariance Matrix [PDF]
EAI Endorsed Transactions on Energy Web, 2020 INTRODUCTION: Nowadays, many machine learning tasks involve learning from imbalanced datasets,leading to the miss-classification of the minority class. One of the state-of-the-art approaches to ”solve” thisproblem at the data level is Synthetic Minority ...
Ireimis Leguen-deVarona +3 more
doaj +1 more source
IDPP: Imbalanced Datasets Pipelines in Pyrus
, 2023 We showcase and demonstrate IDPP, a Pyrus-based tool that offers a collection of pipelines for the analysis of imbalanced datasets. Like Pyrus, IDPP is a web-based, low-code/no-code graphical modelling environment for ML and data analytics applications. On a case study from the medical domain, we solve the challenge of re-using AI/ML models that do not
Amandeep Singh, Olga Minguett
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Data-Centric Optimization Approach for Small, Imbalanced Datasets
Journal of Information and Organizational Sciences, 2023 Data-centric is a newly explored concept, where the attention is given to data optimization methodologies and techniques to improve model performance, rather than focusing on machine learning models and hyperparameter tunning.
Vladislav Tanov
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Boosting methods for multi-class imbalanced data classification: an experimental review
Journal of Big Data, 2020 Since canonical machine learning algorithms assume that the dataset has equal number of samples in each class, binary classification became a very challenging task to discriminate the minority class samples efficiently in imbalanced datasets.
Jafar Tanha +4 more
doaj +1 more source
Bayes classifiers for imbalanced traffic accidents datasets [PDF]
Accident Analysis & Prevention, 2016 Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the ...
Mujalli, R +2 more
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