Results 41 to 50 of about 36,115 (166)
Oversampling Expansion in Wavelet Subspaces
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2011 We find necessary and sufficient conditions for the (shifted) oversampling expansions to hold in wavelet subspaces. In particular, we characterize scaling functions with the (shifted) oversampling property. We also obtain L2 and L∞ norm estimates for the truncation and aliasing errors of the oversampling expansion.
Kwon, KH Kwon, Kil Hyun +1 more
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Generative Adversarial Minority Oversampling [PDF]
2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019 Codes are available at https://github.com/SankhaSubhra ...
Sankha Subhra Mullick +2 more
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Over-sampling imbalanced datasets using the Covariance Matrix [PDF]
EAI Endorsed Transactions on Energy Web, 2020 INTRODUCTION: Nowadays, many machine learning tasks involve learning from imbalanced datasets,leading to the miss-classification of the minority class. One of the state-of-the-art approaches to ”solve” thisproblem at the data level is Synthetic Minority ...
Ireimis Leguen-deVarona +3 more
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Enhanced Skin Lesion Segmentation and Classification Through Ensemble Models
EngThis study addresses challenges in skin cancer detection, particularly issues like class imbalance and the varied appearance of lesions, which complicate segmentation and classification tasks.
Su Myat Thwin, Hyun-Seok Park
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Investigating the Impact of Information Sharing in Human Activity Recognition
Sensors, 2022 The accuracy of Human Activity Recognition is noticeably affected by the orientation of smartphones during data collection. This study utilized a public domain dataset that was specifically collected to include variations in smartphone positioning ...
Muhammad Awais Shafique +1 more
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Bicriteria Oversampling for Imbalanced Data Classification
Procedia Computer Science, 2022 The paper proposes bicriteria oversampling strategy for mining imbalanced data. We use two specialized criteria for oversampling -classification potential and distance from the borderline between minority and majority instances. The potential is to be maximized and the distance minimized.
Joanna Jedrzejowicz, Piotr Jedrzejowicz
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Oversampling ADC: A Review of Recent Design Trends
IEEE AccessOversampling analog-to-digital converters (ADC) serve as the backbone of high-performance, high-precision data interfaces, owing to their remarkable ability to filter out quantization noise. This attribute makes them the preferred choice for applications
Antoine Verreault +2 more
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A Parameter-Free Cleaning Method for SMOTE in Imbalanced Classification
IEEE Access, 2019 Oversampling is an efficient technique in dealing with class-imbalance problem. It addresses the problem by reduplicating or generating the minority class samples to balance the distribution between the samples of the majority and the minority class ...
Yuanting Yan +5 more
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A Study on Dropout Prediction for University Students Using Machine Learning
Applied Sciences, 2023 Student dropout is a serious issue in that it not only affects the individual students who drop out but also has negative impacts on the former university, family, and society together.
Choong Hee Cho +2 more
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Oversampling Techniques for Imbalanced Data in Regression
Expert Systems with Applications, 2023 Our study addresses the challenge of imbalanced regression data in Machine Learning (ML) by introducing tailored methods for different data structures. We adapt K-Nearest Neighbor Oversampling-Regression (KNNOR-Reg), originally for imbalanced classification, to address imbalanced regression in low population datasets, evolving to KNNOR-Deep Regression (
Samir Brahim Belhaouari +4 more
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