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.
makeFilter(name, desc, pkg, supported.tasks, supported.features, fun)Object of class “Filter”.
(character(1))
Identifier for the filter.
(character(1))
Short description of the filter.
(character(1))
Source package where the filter is implemented.
(character)
Task types supported.
(character)
Feature types supported.
(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 nselect features must be calculated, the remaining may be
set to NA or omitted, and thus will not be selected.
the original order will be restored if necessary.
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.
Other filter:
filterFeatures(),
generateFilterValuesData(),
getFilteredFeatures(),
listFilterEnsembleMethods(),
listFilterMethods(),
makeFilterEnsemble(),
makeFilterWrapper(),
plotFilterValues()