A Preliminary Study of SMOTE on Imbalanced Big Datasets When Dealing with Sparse and Dense High Dimensionality.
A. BolívarVicente GarcíaRogelio FlorenciaRoberto AlejoGilberto Rivera ZárateJulia Patricia Sánchez-SolísPublished in: MCPR (2022)
Keyphrases
- high dimensionality
- class imbalance
- imbalanced data
- imbalanced datasets
- high dimensional
- class imbalanced
- sampling methods
- high dimensional datasets
- minority class
- high dimensional data
- dimensionality reduction
- feature selection
- class noise
- feature space
- imbalanced data sets
- class distribution
- cost sensitive learning
- feature selection and classification
- low dimensional
- data dimensionality
- sparse representation
- small sample size
- microarray datasets
- feature selection algorithms
- microarray
- high dimensional spaces
- active learning
- gene expression data
- classification accuracy
- nearest neighbor
- data sets
- pattern recognition
- linear regression
- multi class
- subspace clustering
- training set
- low dimensionality
- neural network