Results 21 to 30 of about 39,918 (213)
IEEE Access, 2020 The rapid development of learning technologies has enabled online learning paradigm to gain great popularity in both high education and K-12, which makes the prediction of student performance become one of the most popular research topics in education ...
Xu Du, Juan Yang, Jui-Long Hung
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Deep Neural Network Ensemble for the Intelligent Fault Diagnosis of Machines Under Imbalanced Data
IEEE Access, 2020 Imbalanced classification using deep learning has attracted much attention in intelligent fault diagnosis of machinery. However, the existing methods use individual deep neural network to extract features and recognize the health conditions under ...
Feng Jia +3 more
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SMOTE-LOF for noise identification in imbalanced data classification
Journal of King Saud University: Computer and Information Sciences, 2022 Imbalanced data typically refers to a condition in which several data samples in a certain problem is not equally distributed, thereby leading to the underrepresentation of one or more classes in the dataset.
Asniar +2 more
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Boundary expansion algorithm of a decision tree induction for an imbalanced dataset [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2017 A decision tree is one of the famous classifiers based on a recursive partitioning algorithm. This paper introduces the Boundary Expansion Algorithm (BEA) to improve a decision tree induction that deals with an imbalanced dataset.
Kesinee Boonchuay +2 more
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Plant Identification in a Combined-Imbalanced Leaf Dataset
IEEE Access, 2022 Plant identification has applications in ethnopharmacology and agriculture. Since leaves are one of a distinguishable feature of a plant, they are routinely used for identification. Recent developments in deep learning have made it possible to accurately
Viraj K. Gajjar +2 more
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Classification Problem in Imbalanced Datasets
, 2020 La classification est une tâche d'exploration de données. Elle vise à extraire des connaissances à partir de grands ensembles de données. Il existe deux types de classification. La première est connue sous le nom de classification complète, et elle est appliquée à des ensembles de données équilibrés. Cependant, lorsqu'elle est appliquée à des ensembles
Aouatef, Mahani,, Baba, Ali, Ahmed Riad
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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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Is Diabetic Retinopathy Grading Biased by Imbalanced Datasets?
, 2022 Diabetic retinopathy (DR) is one of the most severe complications of diabetes and the leading cause of vision loss and even blindness. Retinal screening contributes to early detection and treatment of diabetic retinopathy. This eye disease has five stages, namely normal, mild, moderate, severe and proliferative diabetic retinopathy.
Monteiro, Fernando C., Rufino, José
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Adaptation Proposed Methods for Handling Imbalanced Datasets based on Over-Sampling Technique
Al-Mustansiriyah Journal of Science, 2020 Classification of imbalanced data is an important issue. Many algorithms have been developed for classification, such as Back Propagation (BP) neural networks, decision tree, Bayesian networks etc., and have been used repeatedly in many fields.
Liqaa M. Shoohi, Jamila H. Saud
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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 +2 more
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