Modelling a target attribute by other attributes in the data
is perhaps the most traditional data mining task. When there
are many attributes in the data, one needs to know which of
the attribute(s) are relevant for modelling the target, either
as a group or the one feature that is most appropriate to select
within the model construction process in progress. There are
many approaches for selecting the attribute(s) in machine learning.
We examine various important concepts and approaches that are
used for this purpose and contrast their strengths. Discretization
of numeric attributes is also discussed for its use is prevalent
in many modelling techniques.
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