bibtype J - Journal Article
ARLID 0348726
utime 20240103194028.9
mtime 20101101235959.9
WOS 000281990900001
SCOPUS 78149286082
DOI 10.1109/TPAMI.2010.34.
title (primary) (eng) Evaluating Stability and Comparing Output of Feature Selectors that Optimize Feature Subset Cardinality
specification
page_count 19 s.
serial
ARLID cav_un_epca*0256725
ISSN 0162-8828
title IEEE Transactions on Pattern Analysis and Machine Intelligence
volume_id 32
volume 11 (2010)
page_num 1921-1939
publisher
name IEEE Computer Society
keyword feature selection
keyword feature stability
keyword stability measures
keyword similarity measures
keyword sequential search
keyword individual ranking
keyword feature subset-size optimization
keyword high dimensionality
keyword small sample size
author (primary)
ARLID cav_un_auth*0101197
name1 Somol
name2 Petr
full_dept (cz) Rozpoznávání obrazu
full_dept (eng) Department of Pattern Recognition
department (cz) RO
department (eng) RO
institution UTIA-B
full_dept Department of Pattern Recognition
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101171
name1 Novovičová
name2 Jana
full_dept (cz) Rozpoznávání obrazu
full_dept Department of Pattern Recognition
department (cz) RO
department RO
institution UTIA-B
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2010/RO/somol-0348726.pdf
cas_special
project
project_id 2C06019
agency GA MŠk
country CZ
ARLID cav_un_auth*0216518
project
project_id 1M0572
agency GA MŠk
ARLID cav_un_auth*0001814
project
project_id GA102/08/0593
agency GA ČR
ARLID cav_un_auth*0239567
project
project_id GA102/07/1594
agency GA ČR
ARLID cav_un_auth*0228611
research CEZ:AV0Z10750506
abstract (eng) Stability (robustness) of feature selection methods is a topic of recent interest, yet often neglected importance, with direct impact on the reliability of machine learning systems. We investigate the problem of evaluating the stability of feature selection processes yielding subsets of varying size. We introduce several novel feature selection stability measures and adjust some existing measures in a unifying framework that offers broad insight into the stability problem. We study in detail the properties of considered measures and demonstrate on various examples what information about the feature selection process can be gained. We also introduce an alternative approach to feature selection evaluation in the form of measures that enable comparing the similarity of two feature selection processes. These measures enable comparing, e.g., the output of two feature selection methods or two runs of one method with different parameters.
reportyear 2011
RIV BD
mrcbC52 4 A 4a 20231122134215.1
permalink http://hdl.handle.net/11104/0189168
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arlyear 2010
mrcbTft \nSoubory v repozitáři: somol-0348726.pdf
mrcbU14 78149286082 SCOPUS
mrcbU34 000281990900001 WOS
mrcbU63 cav_un_epca*0256725 IEEE Transactions on Pattern Analysis and Machine Intelligence 0162-8828 1939-3539 Roč. 32 č. 11 2010 1921 1939 IEEE Computer Society