A Feature Selection and Classification Algorithm
Based on Randomized Extraction of Model
Populations
We here introduce a novel classification approach
adopted from the nonlinear model identification framework,
which jointly addresses the feature selection (FS) and classifier
design tasks. The classifier is constructed as a polynomial expansion of the original features and a selection process is applied to
find the relevant model terms. The selection method progressively
refines a probability distribution defined on the model structure
space, by extracting sample models from the current distribution
and using the aggregate information obtained from the evaluation of the population of models to reinforce the probability
of extracting the most important terms. To reduce the initial
search space, distance correlation filtering is optionally applied
as a preprocessing technique. The proposed method is compared
to other well-known FS and classification methods on standard
benchmark problems. Besides the favorable properties of the
method regarding classification accuracy, the obtained models
have a simple structure, easily amenable to interpretation and
analysis.
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This product was added to our catalog on Wednesday 13 June, 2018.