Feature selection using symmetric uncertainty and hybrid optimization for high-dimensional data.
Lin SunShujing SunWeiping DingXinyue HuangPeiyi FanKunyu LiLeqi ChenPublished in: Int. J. Mach. Learn. Cybern. (2023)
Keyphrases
- high dimensional data
- high dimensionality
- dimensionality reduction
- feature selection
- low dimensional
- dimension reduction
- high dimensional
- nearest neighbor
- data sets
- subspace clustering
- high dimensions
- data analysis
- gene expression data
- similarity search
- data points
- low rank
- text categorization
- input space
- linear discriminant analysis
- high dimensional datasets
- clustering high dimensional data
- subspace learning
- text data
- data distribution
- small sample size
- manifold learning
- principal component analysis
- high dimensional spaces
- variable selection
- feature extraction
- nonlinear dimensionality reduction
- high dimensional data sets
- microarray data
- lower dimensional
- missing values
- text classification
- knn
- support vector
- selected features
- multi task
- locally linear embedding
- feature subset
- feature space
- variable weighting