Attribute Selection

Selecting attributes (features) from a set of instances is important for getting the best result out of a classification or clustering. Attribute selection reduces the number of attributes and thereby can speed up the runtime of the algorithms. It also avoids processing too many attributes when only a certain subset is essential for building a good model.

For attribute selection you need to apply a search and an evaluation method on a dataset.

Search methods are defined in the Weka::AttributeSelection::Search module. There are search methods for subset search and individual attribute search.

Evaluators are defined in the Weka::AttributeSelection::Evaluator module. Corresponding to search method types there are two evalutor types for subset search and individual search.

The search methods and evaluators from each category can be combined to perform an attribute selection.

Classes for attribute subset selection:

Search	Evaluators
BestFirst, GreedyStepwise	CfsSubset, WrapperSubset

Classes for individual attribute selection:

Search	Evaluators
Ranker	CorrelationAttribute, GainRatioAttribute, InfoGainAttribute, OneRAttribute, ReliefFAttribute, SymmetricalUncertAttribute

An attribute selection can either be performed with the Weka::AttributeSelection::AttributeSelection class:

instances = Weka::Core::Instances.from_arff('weather.arff')

selection           = Weka::AttributeSelection::AttributeSelection.new
selection.search    = Weka::AttributeSelection::Search::Ranker.new
selection.evaluator = Weka::AttributeSelection::Evaluator::PricipalComponents.new

selection.select_attribute(instances)
puts selection.summary

Or you can use the supervised AttributeSelection filter to directly filter instances:

instances = Weka::Core::Instances.from_arff('weather.arff')
search    = Weka::AttributeSelection::Search::Ranker.new
evaluator = Weka::AttributeSelection::Evaluator::PricipalComponents.new

filter = Weka::Filters::Supervised::Attribute::AttributeSelection.build do
  use_search    search
  use_evaluator evaluator
end

filtered_instances = instances.apply_filter(filter)