A critical assessment of imbalanced class distribution problem: the case of predicting freshmen student attrition [PDF]
, 2013 Predicting student attrition is an intriguing yet challenging problem for any academic institution. Class-imbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification
Thammasiri, Dech +3 more
core +1 more source
, 2023 The sudden resignation of core employees often brings losses to companies in various aspects. Traditional employee turnover theory cannot analyze the unbalanced data of employees comprehensively, which leads the company to make wrong decisions.
Zhaotian Li (16846072) +1 more
core +1 more source
Hepatology, EarlyView., 2022 The role of SMLR1 in lipid metabolism (high fat + cholesterol diet in mice) Abstract Background and Aims The assembly and secretion of VLDL from the liver, a pathway that affects hepatic and plasma lipids, remains incompletely understood. We set out to identify players in the VLDL biogenesis pathway by identifying genes that are co‐expressed with the ...
Willemien van Zwol +22 more
wiley +1 more source
On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems [PDF]
, 2011 The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data.
Xia Hong +10 more
core +1 more source
SIA-SMOTE: A SMOTE-based Oversampling Method with Better Interpolation on High-Dimensional Data by Using a Siamese Network [PDF]
, 2023 SMOTE is an effective method for balancing imbalanced datasets by interpolating between existing samples in the minority class. However, if the synthetic samples generated through interpolation are based on noisy data points, then they may also be noisy ...
Gan, John +2 more
core +1 more source
Horse surgery and survival prediction with artificial intelligence models: performance comparison of original, imputed, balanced, and feature- selected datasets [PDF]
Kafkas Universitesi Veteriner Fakültesi DergisiArtificial intelligence (AI) technology, while less advanced than in human medicine, holds significant potential in the field of veterinary medicine.
Pınar CİHAN
doaj +1 more source
Impact of Data Balancing and Feature Selection on Machine Learning-based Network Intrusion Detection
JOIV: International Journal on Informatics Visualization, 2023 Unbalanced datasets are a common problem in supervised machine learning. It leads to a deeper understanding of the majority of classes in machine learning.
Azhari Shouni Barkah +3 more
doaj +1 more source
Classification of Brain Tumors on MRI Images Using DenseNet and Support Vector Machine
JOIV: International Journal on Informatics Visualization, 2022 The brain is a vital organ in the human body, performing various functions. The brain has always played a major role in the processing of sensory information, the production of muscular activity, and the performance of high-level cognitive functions ...
Agus Eko Minarno +4 more
doaj +1 more source
Predict COVID-19 Spreading With C-SMOTE [PDF]
, 2021 Data continuously gathered monitoring the spreading of the COVID-19 pandemic form an unbounded flow of data. Accurately forecasting if the infections will increase or decrease has a high impact, but it is challenging because the pandemic spreads and ...
A. Bernardo, E. Della Valle
core +3 more sources
A-SMOTE: A New Preprocessing Approach for Highly Imbalanced Datasets by Improving SMOTE
International Journal of Computational Intelligence Systems, 2019 Imbalance learning is a challenging task for most standard machine learning algorithms. The Synthetic Minority Oversampling Technique (SMOTE) is a well-known preprocessing approach for handling imbalanced datasets, where the minority class is oversampled by producing synthetic examples in feature vector rather than data space.
Ahmed Saad Hussein +3 more
openaire +2 more sources