Comparison of unsupervised feature selection methods for high-dimensional regression problems in prediction of peptide binding affinity.
Ferdi SaracVolkan UslanHuseyin SekerAhmed BouridanePublished in: EMBC (2015)
Keyphrases
- regression problems
- high dimensional
- major histocompatibility complex
- input space
- high dimensional data
- genetic programming
- linear regression
- cross validation
- binding peptides
- mhc class ii
- prediction accuracy
- multi task
- dimensionality reduction
- random forests
- data points
- low dimensional
- machine learning
- hyperparameters
- pairwise
- support vector
- learning machines
- em algorithm
- parameter space
- least squares
- semi supervised
- multi class
- pattern recognition
- decision trees
- learning algorithm