Results 11 to 20 of about 106,507 (280)
SSMFN: a fused spatial and sequential deep learning model for methylation site prediction [PDF]
PeerJ Computer Science, 2021 Background Conventional in vivo methods for post-translational modification site prediction such as spectrophotometry, Western blotting, and chromatin immune precipitation can be very expensive and time-consuming.
Favorisen Rosyking Lumbanraja +4 more
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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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Federated Learning on Clinical Benchmark Data: Performance Assessment
Journal of Medical Internet Research, 2020 BackgroundFederated learning (FL) is a newly proposed machine-learning method that uses a decentralized dataset. Since data transfer is not necessary for the learning process in FL, there is a significant advantage in protecting personal privacy ...
Lee, Geun Hyeong, Shin, Soo-Yong
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Multilayer Feedforward Neural Network for Internet Traffic Classification.
International Journal of Interactive Multimedia and Artificial Intelligence, 2020 Recently, the efficient internet traffic classification has gained attention in order to improve service quality in IP networks. But the problem with the existing solutions is to handle the imbalanced dataset which has high uneven distribution of flows ...
N. Manju, B. S. Harish, N. Nagadarshan
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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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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 +5 more
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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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Semantic concept detection in imbalanced datasets based on different under-sampling strategies [PDF]
, 2011 Semantic concept detection is a very useful technique for developing powerful retrieval or filtering systems for multimedia data. To date, the methods for concept detection have been converging on generic classification schemes.
Foley, Colum +3 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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