Unbiased Prediction and Feature Selection in High-Dimensional Survival Regression.
Michael LaimighoferJan KrumsiekFlorian A. BüttnerFabian J. TheisPublished in: J. Comput. Biol. (2016)
Keyphrases
- high dimensional
- feature selection
- survival analysis
- high dimensionality
- stepwise regression
- dimensionality reduction
- prediction model
- regression model
- variable selection
- prediction accuracy
- support vector machines for classification
- supervised feature selection
- support vector
- model selection
- feature space
- small sample
- linear regression model
- proportional hazards model
- machine learning
- regression problems
- gene expression data
- high dimensional data
- input space
- predictive clustering trees
- linear regression
- dimension reduction
- multi variate
- kaplan meier
- breast cancer
- mutual information
- low dimensional
- text classification
- text categorization
- similarity search
- microarray data
- information gain
- prediction error
- multi class
- neural network
- kernel function
- feature extraction
- data points
- high dimension
- support vector regression
- radial basis function network
- feature subset
- feature ranking
- feature set
- target variable
- logistic regression
- automatic relevance determination
- gaussian processes
- classification accuracy
- feature vectors
- decision trees
- multi task