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Imbalanced Ensemble Classifier for Learning from Imbalanced Business School Dataset [PDF]
International Journal of Mathematical, Engineering and Management Sciences, 2019 Private business schools in India face a regular problem of picking quality students for their MBA programs to achieve the desired placement percentage. Generally, such datasets are biased towards one class, i.e., imbalanced in nature.
Tanujit Chakraborty
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A Comparative Study of the Use of Stratified Cross-Validation and Distribution-Balanced Stratified Cross-Validation in Imbalanced Learning. [PDF]
Sensors (Basel), 2023 Szeghalmy S, Fazekas A.
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A Method for Analyzing the Performance Impact of Imbalanced Binary Data on Machine Learning Models
Axioms, 2022 Machine learning models may not be able to effectively learn and predict from imbalanced data in the fields of machine learning and data mining. This study proposed a method for analyzing the performance impact of imbalanced binary data on machine ...
Ming Zheng +5 more
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Applied Sciences, 2022 The classification of imbalanced and overlapping data has provided customary insight over the last decade, as most real-world applications comprise multiple classes with an imbalanced distribution of samples.
Zafar Mahmood +6 more
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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 +3 more
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An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images
Sensors, 2020 Imbalanced learning is a common problem in remote sensing imagery-based land-use and land-cover classifications. Imbalanced learning can lead to a reduction in classification accuracy and even the omission of the minority class.
Fei Sun +6 more
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Prediction of hematocrit through imbalanced dataset of blood spectra
Healthcare Technology Letters, 2021 In spite of machine learning has been successfully used in a wide range of healthcare applications, there are several parameters that could influence the performance of a machine learning system.
Cristoforo Decaro +3 more
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IEEE Access, 2021 Imbalanced data classification is one of the most important tasks in the field of machine learning because abnormality, which is usually of our interest, appears less frequently than normality in real-world systems.
Yeontark Park, Jong-Seok Lee
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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
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A Novel Imbalanced Ensemble Learning in Software Defect Predication
IEEE Access, 2021 With the availability of high-speed Internet and the advent of Internet of Things devices, modern software systems are growing in both size and complexity. Software defect prediction (SDP) guarantees the high quality of such complex systems. However, the
Jianming Zheng +4 more
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