A Novel GA-based Feature Selection Approach for High Dimensional Data.
Claudio De StefanoFrancesco FontanellaAlessandra Scotto di FrecaPublished in: GECCO (Companion) (2016)
Keyphrases
- high dimensional data
- feature selection
- high dimensionality
- dimensionality reduction
- dimension reduction
- low dimensional
- high dimensional
- gene expression data
- nearest neighbor
- data analysis
- high dimensions
- data points
- similarity search
- input space
- data distribution
- feature space
- subspace clustering
- original data
- data sets
- text categorization
- subspace learning
- linear discriminant analysis
- small sample size
- missing values
- input data
- feature selection algorithms
- high dimensional data sets
- clustering high dimensional data
- genetic algorithm
- feature extraction
- text classification
- feature set
- high dimensional datasets
- manifold learning
- similarity measure
- machine learning
- nonlinear dimensionality reduction
- high dimensional spaces
- dimensional data
- genetic algorithm ga
- preprocessing step
- support vector
- pattern recognition
- semi supervised
- principal component analysis
- text data
- selected features
- lower dimensional
- knn
- support vector machine
- variable selection
- clustering algorithm
- locally linear embedding
- computer vision
- neural network