0342820 - ÚTIA 2011 RIV HR eng M - Monography Chapter
Somol, Petr - Novovičová, Jana - Pudil, Pavel
Efficient Feature Subset Selection and Subset Size Optimization.
Pattern Recognition, Recent Advances. Vukovar, Croatia: In-Teh, 2010 - (Herout, A.), s. 75-98. ISBN 978-953-7619-90-9
R&D Projects: GA MŠMT 1M0572; GA ČR GA102/08/0593; GA ČR GA102/07/1594
Grant - others:GA MŠk(CZ) 2C06019
Institutional research plan: CEZ:AV0Z10750506
Keywords : dimensionality reduction * pattern recognition * machine learning * feature selection * optimization * subset search * classification
Subject RIV: BD - Theory of Information
Result website:
http://library.utia.cas.cz/separaty/2010/RO/somol-efficient feature subset selection and subset size optimization.pdf

A broad class of decision-making problems can be solved by learning approach. This can be a feasible alternative when neither an analytical solution exists nor the mathematical model can be constructed. In these cases the required knowledge can be gained from the past data which form the so-called learning or training set. Then the formal apparatus of statistical pattern recognition can be used to learn the decision-making. The first and essential step of statistical pattern recognition is to solve the problem of feature selection (FS) or more generally dimensionality reduction (DR). The chapter summarizes the state of art in feature selection, addressing key topics including: FS categorization, FS criteria, FS search strategies, FS stability.
Permanent Link: http://hdl.handle.net/11104/0185446