Creates and registers custom feature filters. Implemented filters
can be listed with listFilterMethods. Additional
documentation for the fun parameter specific to each filter can
be found in the description.
Arguments
- name
- ( - character(1))
 Identifier for the filter.
- desc
- ( - character(1))
 Short description of the filter.
- pkg
- ( - character(1))
 Source package where the filter is implemented.
- supported.tasks
- (character) 
 Task types supported.
- supported.features
- (character) 
 Feature types supported.
- fun
- ( - function(task, nselect, ...)
 Function which takes a task and returns a named numeric vector of scores, one score for each feature of- task. Higher scores mean higher importance of the feature. At least- nselectfeatures must be calculated, the remaining may be set to- NAor omitted, and thus will not be selected. the original order will be restored if necessary.
References
Kira, Kenji and Rendell, Larry (1992). The Feature Selection Problem: Traditional Methods and a New Algorithm. AAAI-92 Proceedings.
Kononenko, Igor et al. Overcoming the myopia of inductive learning algorithms with RELIEFF (1997), Applied Intelligence, 7(1), p39-55.
See also
Other filter: 
filterFeatures(),
generateFilterValuesData(),
getFilteredFeatures(),
listFilterEnsembleMethods(),
listFilterMethods(),
makeFilterEnsemble(),
makeFilterWrapper(),
plotFilterValues()
