Feature Selection for Improving the Usability of Classification Results of High-Dimensional Data.
Christian SassenbergChristian WeberMadjid FathiAlexander HollandRalf MontinoPublished in: DMIN (2008)
Keyphrases
- high dimensional data
- high dimensionality
- feature selection
- dimensionality reduction
- dimension reduction
- high dimensional
- low dimensional
- small sample size
- classification accuracy
- feature space
- nearest neighbor
- data sets
- feature extraction
- regression problems
- text classification
- support vector
- linear discriminant analysis
- similarity search
- manifold learning
- high dimensional spaces
- machine learning
- gene expression data
- subspace clustering
- data analysis
- variable selection
- feature selection algorithms
- pattern recognition
- high dimensional datasets
- input space
- high dimensions
- multivariate temporal data
- high dimensional feature spaces
- sparse representation
- unsupervised learning
- data points
- feature subset
- clustering high dimensional data
- lower dimensional
- dimensional data
- text categorization
- feature set
- support vector machine
- subspace learning
- neural network
- missing values
- decision trees
- nonlinear dimensionality reduction
- input data
- model selection
- classification algorithm
- knn
- principal component analysis
- face recognition
- multi dimensional
- real world
- variable weighting