Learning from class-imbalanced data: Review of methods and applications
Rare events, especially those that could potentially negatively impact society, often require
humans' decision-making responses. Detecting rare events can be viewed as a prediction …
humans' decision-making responses. Detecting rare events can be viewed as a prediction …
Oversampling techniques for bankruptcy prediction: Novel features from a transaction dataset
In recent years, weakened by the fall of economic growth, many enterprises fell into the crisis
caused by financial difficulties. Bankruptcy prediction, a machine learning model, is a great …
caused by financial difficulties. Bankruptcy prediction, a machine learning model, is a great …
Learning deep landmarks for imbalanced classification
We introduce a deep imbalanced learning framework called learning DEep Landmarks in
laTent spAce (DELTA). Our work is inspired by the shallow imbalanced learning approaches …
laTent spAce (DELTA). Our work is inspired by the shallow imbalanced learning approaches …
[图书][B] Dealing with imbalanced and weakly labelled data in machine learning using fuzzy and rough set methods
S Vluymans - 2019 - Springer
This book is based on my Ph. D. dissertation completed at Ghent University (Belgium) and
the University of Granada (Spain) in June 2018. It focuses on classification. The goal is to …
the University of Granada (Spain) in June 2018. It focuses on classification. The goal is to …
SAERMA: stacked autoencoder rule mining algorithm for the interpretation of epistatic interactions in GWAS for extreme obesity
One of the most important challenges in the analysis of high-throughput genetic data is the
development of efficient computational methods to identify statistically significant Single …
development of efficient computational methods to identify statistically significant Single …
Bosco: Boosting corrections for genome-wide association studies with imbalanced samples
In genome-wide association studies (GWAS), the acquired sequential data may exhibit
imbalance structure: abundant control vs. limited case samples. Such sample imbalance …
imbalance structure: abundant control vs. limited case samples. Such sample imbalance …
[HTML][HTML] aXonica: a support package for MRI based Neuroimaging
Abstract Magnetic Resonance Imaging (MRI) assists in studying the nervous system. MRI
scans undergo significant processing before presenting the final images to medical …
scans undergo significant processing before presenting the final images to medical …
Comparison of sampling methods using machine learning and deep learning algorithms with an imbalanced data set for the prevention of violence against physicians
The aim of this study is to compare sampling methods using machine and deep learning
algorithms with a small and imbalanced data set for the prevention of violence against …
algorithms with a small and imbalanced data set for the prevention of violence against …
[图书][B] Computational Reconstruction of Missing Data in Biological Research
F Bao - 2021 - books.google.com
The emerging biotechnologies have significantly advanced the study of biological
mechanisms. However, biological data usually contain a great amount of missing …
mechanisms. However, biological data usually contain a great amount of missing …
Computational Recovery of Sample Missings
F Bao, F Bao - Computational Reconstruction of Missing Data in …, 2021 - Springer
Many problems in the real world include uneven label distributions where the proportion of
samples of one type in the data is greatly different from the proportion of samples of other …
samples of one type in the data is greatly different from the proportion of samples of other …