| Name : R-RaSEn
| |
| Version : 3.0.0
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp156.13.1
| Date : 2024-09-10 03:07:47
|
| Group : Development/Libraries/Other
| Source RPM : R-RaSEn-3.0.0-lp156.13.1.src.rpm
|
| Size : 4.02 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : Random Subspace Ensemble Classification and Variable Screening
|
Description :
We propose a general ensemble classification framework, RaSE algorithm,
for the sparse classification problem. In RaSE algorithm, for each weak
learner, some random subspaces are generated and the optimal one is
chosen to train the model on the basis of some criterion. To be adapted
to the problem, a novel criterion, ratio information criterion (RIC) is
put up with based on Kullback-Leibler divergence. Besides minimizing
RIC, multiple criteria can be applied, for instance, minimizing
extended Bayesian information criterion (eBIC), minimizing training
error, minimizing the validation error, minimizing the cross-validation
error, minimizing leave-one-out error. There are various choices of
base classifier, for instance, linear discriminant analysis, quadratic
discriminant analysis, k-nearest neighbour, logistic regression,
decision trees, random forest, support vector machines. RaSE algorithm
can also be applied to do feature ranking, providing us the importance
of each feature based on the selected percentage in multiple subspaces.
RaSE framework can be extended to the general prediction framework,
including both classification and regression. We can use the selected
percentages of variables for variable screening. The latest version
added the variable screening function for both regression and
classification problems.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.6/x86_64 |
