Imbalanced dataset for benchmarking
, 2023 The different algorithms of the "imbalanced-learn" toolbox are evaluated on a set of common dataset, which are more or less balanced. These benchmark have been proposed in Ding, Zejin, "Diversified Ensemble Classifiers for H ighly Imbalanced Data ...
Oliveira, Dayvid V. R. +3 more
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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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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.
Guo, Jinlin +7 more
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Classification results of machine learning models using TF-IDF on imbalanced dataset.
, 2022 Classification results of machine learning models using TF-IDF on imbalanced dataset.
Furqan Rustam (10196722) +6 more
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Classification results of machine learning models using BoW on imbalanced dataset.
, 2022 Classification results of machine learning models using BoW on imbalanced dataset.
Furqan Rustam (10196722) +6 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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A kernel-based two-class classifier for imbalanced data sets [PDF]
, 2007 Many kernel classifier construction algorithms adopt classification accuracy as performance metrics in model evaluation. Moreover, equal weighting is often applied to each data sample in parameter estimation.
Hong, X., Harris, C.J., Chen, S.
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Build and experiment different BILSTM nodes on the imbalanced dataset with fine tuning.
, 2023 Build and experiment different BILSTM nodes on the imbalanced dataset with fine tuning.
Alaa Alomari (17403786) +2 more
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A critical assessment of imbalanced class distribution problem: the case of predicting freshmen student attrition [PDF]
, 2013 Predicting student attrition is an intriguing yet challenging problem for any academic institution. Class-imbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification
Thammasiri, Dech +3 more
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Results of BILSTM for rare classes for the imbalanced dataset with different reweighting factors.
, 2023 Results of BILSTM for rare classes for the imbalanced dataset with different reweighting factors.
Alaa Alomari (17403786) +2 more
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