Advances in data preprocessing for biomedical data fusion: An overview of the methods, challenges, and prospects
Due to the proliferation of biomedical imaging modalities, such as Photoacoustic
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …
Stop oversampling for class imbalance learning: A review
For the last two decades, oversampling has been employed to overcome the challenge of
learning from imbalanced datasets. Many approaches to solving this challenge have been …
learning from imbalanced datasets. Many approaches to solving this challenge have been …
[HTML][HTML] An empirical survey of data augmentation for time series classification with neural networks
In recent times, deep artificial neural networks have achieved many successes in pattern
recognition. Part of this success can be attributed to the reliance on big data to increase …
recognition. Part of this success can be attributed to the reliance on big data to increase …
Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network
Abstract (Aim) COVID-19 is an infectious disease spreading to the world this year. In this
study, we plan to develop an artificial intelligence based tool to diagnose on chest CT …
study, we plan to develop an artificial intelligence based tool to diagnose on chest CT …
[HTML][HTML] LoRAS: An oversampling approach for imbalanced datasets
Abstract The Synthetic Minority Oversampling TEchnique (SMOTE) is widely-used for the
analysis of imbalanced datasets. It is known that SMOTE frequently over-generalizes the …
analysis of imbalanced datasets. It is known that SMOTE frequently over-generalizes the …
On supervised class-imbalanced learning: An updated perspective and some key challenges
S Das, SS Mullick, I Zelinka - IEEE Transactions on Artificial …, 2022 - ieeexplore.ieee.org
The problem of class imbalance has always been considered as a significant challenge to
traditional machine learning and the emerging deep learning research communities. A …
traditional machine learning and the emerging deep learning research communities. A …
[HTML][HTML] Rdpvr: Random data partitioning with voting rule for machine learning from class-imbalanced datasets
Since most classifiers are biased toward the dominant class, class imbalance is a
challenging problem in machine learning. The most popular approaches to solving this …
challenging problem in machine learning. The most popular approaches to solving this …
Diversity based imbalance learning approach for software fault prediction using machine learning models
P Manchala, M Bisi - Applied Soft Computing, 2022 - Elsevier
The Software fault prediction (SFP) target is to distinguish between faulty and non-faulty
modules. The prediction model's performance is vulnerable to the class imbalance issue in …
modules. The prediction model's performance is vulnerable to the class imbalance issue in …
An efficient method to determine sample size in oversampling based on classification complexity for imbalanced data
D Lee, K Kim - Expert Systems with Applications, 2021 - Elsevier
Resampling, one of the approaches to handle class imbalance, is widely used alone or in
combination with other approaches, such as cost-sensitive learning and ensemble learning …
combination with other approaches, such as cost-sensitive learning and ensemble learning …
Diagnosis of secondary pulmonary tuberculosis by an eight-layer improved convolutional neural network with stochastic pooling and hyperparameter optimization
To more efficiently diagnose secondary pulmonary tuberculosis, we build an improved
convolutional neural network (ICNN) based on recent deep learning technologies. First, a 12 …
convolutional neural network (ICNN) based on recent deep learning technologies. First, a 12 …