Incremental feature search.
A scalar Measure is computed multiple times on variations of a certain dataset. These measures are in turn used to incrementally select important features. Starting with an empty feature set the dataset measure is first computed for each single feature. A number of features is selected based on the resulting data measure map (using an ElementSelector).
Next the dataset measure is computed again using each feature in addition to the already selected feature set. Again the ElementSelector is used to select more features.
For each feature selection the transfer error on some testdatset is computed. This procedure is repeated until a given StoppingCriterion is reached.
Notes
Available conditional attributes:
(Conditional attributes enabled by default suffixed with +)
Initialize incremental feature search
Parameters : | feature_measure : Measure
performance_measure : Measure
splitter: Splitter :
enable_ca : None or list of str
disable_ca : None or list of str
fmeasure : Measure
pmeasure : Measure
bestdetector : Functor
stopping_criterion : Functor
fselector : Functor train_clf : bool
filler : optional
auto_train : bool
force_train : bool
space: str, optional :
postproc : Node instance, optional
descr : str
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