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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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Prediction of hematocrit through imbalanced dataset of blood spectra [PDF]
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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An Imbalanced Generative Adversarial Network-Based Approach for Network Intrusion Detection in an Imbalanced Dataset [PDF]
Sensors, 2023 In modern networks, a Network Intrusion Detection System (NIDS) is a critical security device for detecting unauthorized activity. The categorization effectiveness for minority classes is limited by the imbalanced class issues connected with the dataset.
Yamarthi Narasimha Rao +1 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.
Davis, Randall +2 more
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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 +4 more
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Applied Sciences, 2022 A homemade defective model of an induction motor was created by the laboratory team to acquire the vibration acceleration signals of five operating states of an induction motor under different loads. Two major learning models, namely a deep convolutional
Hong-Chan Chang +3 more
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Applied Sciences, 2021 Human activity recognition (HAR) is the study of the identification of specific human movement and action based on images, accelerometer data and inertia measurement unit (IMU) sensors.
Xiang Yang Lim +2 more
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Applied Sciences, 2021 The Massachusetts Eye and Ear Infirmary (MEEI) database is an international-standard training database for voice pathology detection (VPD) systems. However, there is a class-imbalanced distribution in normal and pathological voice samples and different ...
Ziqi Fan +4 more
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Conversion of adverse data corpus to shrewd output using sampling metrics
Visual Computing for Industry, Biomedicine, and Art, 2020 An imbalanced dataset is commonly found in at least one class, which are typically exceeded by the other ones. A machine learning algorithm (classifier) trained with an imbalanced dataset predicts the majority class (frequently occurring) more than the ...
Shahzad Ashraf +4 more
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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 +2 more
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