A Novel Automated Framework for QSAR Modeling of Highly Imbalanced Leishmania High-Throughput Screening Data
Sign Up to like & getrecommendations! Published in 2021 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.0c01439
Abstract: In silico prediction of antileishmanial activity using quantitative structure-activity relationship (QSAR) models has been developed on limited and small datasets. Nowadays, the availability of large and diverse high-throughput screening data provides an opportunity to the… read more here.
Keywords: highly imbalanced; consensus; high throughput; model ... See more keywords
Maximal Information Coefficient-Based Undersampling Method for Highly-Imbalanced Learning
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Access"
DOI: 10.1109/access.2025.3525475
Abstract: Learning from highly-imbalanced datasets is still a big challenge in the field of machine learning because models created by general learning algorithms are weak in recognizing the samples from the minority class correctly. Undersampling is… read more here.
Keywords: maximal information; undersampling method; highly imbalanced; information coefficient ... See more keywords
Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal of Biomedical and Health Informatics"
DOI: 10.1109/jbhi.2022.3187215
Abstract: Melanoma is a fatal skin cancer that is curable and has dramatically increasing survival rate when diagnosed at early stages. Learning-based methods hold significant promise for the detection of melanoma from dermoscopic images. However, since… read more here.
Keywords: deep clustering; highly imbalanced; triplet loss; triplet ... See more keywords
Data reduction techniques for highly imbalanced medicare Big Data
Sign Up to like & getrecommendations! Published in 2024 at "Journal of Big Data"
DOI: 10.1186/s40537-023-00869-3
Abstract: In the domain of Medicare insurance fraud detection, handling imbalanced Big Data and high dimensionality remains a significant challenge. This study assesses the combined efficacy of two data reduction techniques: Random Undersampling (RUS), and a… read more here.
Keywords: medicare; reduction; highly imbalanced; big data ... See more keywords
Highly Imbalanced Classification of Gout Using Data Resampling and Ensemble Method
Sign Up to like & getrecommendations! Published in 2024 at "Algorithms"
DOI: 10.3390/a17030122
Abstract: Gout is one of the most painful diseases in the world. Accurate classification of gout is crucial for diagnosis and treatment which can potentially save lives. However, the current methods for classifying gout periods have… read more here.
Keywords: classification; gout; method; imbalanced gout ... See more keywords
Comparison between Statistical Models and Machine Learning Methods on Classification for Highly Imbalanced Multiclass Kidney Data
Sign Up to like & getrecommendations! Published in 2020 at "Diagnostics"
DOI: 10.3390/diagnostics10060415
Abstract: This study aims to compare the classification performance of statistical models on highly imbalanced kidney data. The health examination cohort database provided by the National Health Insurance Service in Korea is utilized to build models… read more here.
Keywords: classification; machine learning; highly imbalanced; performance ... See more keywords