Cargando…
Iterative Nearest Neighborhood Oversampling in Semisupervised Learning from Imbalanced Data
Transductive graph-based semisupervised learning methods usually build an undirected graph utilizing both labeled and unlabeled samples as vertices. Those methods propagate label information of labeled samples to neighbors through their edges in order to get the predicted labels of unlabeled samples...
Autores principales: | Li, Fengqi, Yu, Chuang, Yang, Nanhai, Xia, Feng, Li, Guangming, Kaveh-Yazdy, Fatemeh |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3725769/ https://www.ncbi.nlm.nih.gov/pubmed/23935439 http://dx.doi.org/10.1155/2013/875450 |
Ejemplares similares
-
Selective oversampling approach for strongly imbalanced data
por: Gnip, Peter, et al.
Publicado: (2021) -
An oversampling method for multi-class imbalanced data based on composite weights
por: Deng, Mingyang, et al.
Publicado: (2021) -
Imbalanced classification for protein subcellular localization with multilabel oversampling
por: Rana, Priyanka, et al.
Publicado: (2022) -
A Boundary-Information-Based Oversampling Approach to Improve Learning Performance for Imbalanced Datasets
por: Li, Der-Chiang, et al.
Publicado: (2022) -
Evolutionary Mahalanobis Distance-Based Oversampling for Multi-Class Imbalanced Data Classification
por: Yao, Leehter, et al.
Publicado: (2021)