Fast Adapting Ensemble: A New Algorithm for Mining Data Streams with Concept Drift
: Data stream.
: Each training example is formed by a vector and a discrete
value , named label and taken from a finite set named class.
: Vector of base classifiers.
: Vector of weights of base classifiers.
: Vector of status of base classifiers (Active or inactive).
: Vector of concepts associated with base classifiers.
: Set of examples necessary for building a new base classifier.
: Set of examples necessary for testing the ensemble.
ne: Number of examples necessary for building a new base classifier.
nt: Number of examples necessary for testing the ensemble.
General FAE algorithm
While (nextexample) // Start the training of the ensemble.
if ( mod nt = 0) // each nt examples weights and status are updated.
end if // End of the update block
if ( mod ne = 0) //each ne examples creating a new base classifier is analyzed.
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