Databases Reference
In-Depth Information
Finally, we outline few research directions in the area of feature
selection.
criteria
113
(1) Define
standardized
evaluation
to
enable
systematic
comparison of existing and novel approaches.
(2) Scale up intelligent focusing approaches by combining technologies from
machine learning research and the database community.
(3) Develop more intelligent focusing solutions that provide data reduction
techniques beyond pure statistical sampling
46
and make use of the
specific characteristics of concrete contexts in data mining.
(4) A more rigorous investigation is required to formulate and implement
unsupervised feature selection as a multi-objective problem.
Much work still remains to be done. Instance and feature selection
corresponds to scaling down data and reduce the feature space. When we
understand better instance and feature selection, it is natural to investigate
if this work can be combined with other lines of research in overcoming the
problem of huge amounts of data.
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