Results 11 to 20 of about 39,020 (265)

Impact of imbalanced features on large datasets

Frontiers in Big Data
The exponential growth of image and video data motivates the need for practical real-time content-based searching algorithms. Features play a vital role in identifying objects within images.
Waleed Albattah, Rehan Ullah Khan
doaj   +3 more sources

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, Narek Davtyan, Markus Wolfien, Mariam Nassar, Olaf Wolkenhauer   +4 more
openaire   +3 more sources

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, Popescu, Adrian, Hudelot, Céline   +2 more
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Distribution-sensitive learning for imbalanced datasets [PDF]

2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2013
Many real-world face and gesture datasets are by nature imbalanced across classes. Conventional statistical learning models (e.g., SVM, HMM, CRY), however, are sensitive to imbalanced datasets. In this paper we show how an imbalanced dataset affects the performance of a standard learning algorithm, and propose a distribution-sensitive prior to deal ...
Song, Yale, Morency, Louis-Philippe, Davis, Randall   +2 more
openaire   +3 more sources

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
openaire   +2 more sources

Offline Reinforcement Learning with Imbalanced Datasets

CoRR, 2023
The prevalent use of benchmarks in current offline reinforcement learning (RL) research has led to a neglect of the imbalance of real-world dataset distributions in the development of models. The real-world offline RL dataset is often imbalanced over the state space due to the challenge of exploration or safety considerations. In this paper, we specify
Li Jiang 0008, Sijie Chen, Jielin Qiu, Haoran Xu 0003, Wai Kin Chan, Zhao Ding   +5 more
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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, Mavrides, Lambros, Tryfou, Georgina, Tolias, Kyriakos, Panousis, Konstantinos P., Chatzis, Sotirios P., Theodoridis, Sergios   +6 more
openaire   +5 more sources

Oversampling Method To Handling Imbalanced Datasets Problem In Binary Logistic Regression Algorithm

IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2020
The class imbalance is a condition when one class has a higher percentage than the other then it can affect the accuracy. One method in data mining that can be used to classification is logistic regression method.
Windyaning Ustyannie, Suprapto Suprapto
doaj   +1 more source

Using deep learning for trajectory classification in imbalanced dataset

Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2021
Deep learning has gained much popularity in the past years due to GPU advancements, cloud computing improvements, and its supremacy, considering the accuracy results when trained on massive datasets.
Nicksson Ckayo Arrais de Freitas, Ticiana L. Coelho da Silva, José Antônio Fernandes de Macêdo, Leopoldo Melo Júnioer   +3 more
doaj   +1 more source

On the Classification of Imbalanced Datasets

International Journal of Computer Applications, 2012
In recent research the classifications of imbalanced data sets have received considerable attention. It is natural that due to the class imbalance the classifier tends to favour majority class. In this paper we investigate the performance of different methods for handling data imbalance in the microcalcification classification which is a classical ...
H. S. Sheshadri, Arun KumarM.N
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