bibtype V - Research Report
ARLID 0357271
utime 20240103194938.6
mtime 20110304235959.9
title (primary) (eng) Introduction to Feature Selection Toolbox 3 – The C++ Library for Subset Search, Data Modeling and Classification
publisher
place Praha
name ÚTIA
pub_time 2010
specification
page_count 13 s.
edition
name Research Report
volume_id 2287
keyword feature selection
keyword software library
keyword subset search
keyword attribute selection
keyword variable selection
keyword optimization
keyword machine learning
keyword classification
keyword pattern recognition
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*0213290
name1 Vácha
name2 Pavel
full_dept (cz) Rozpoznávání obrazu
full_dept Department of Pattern Recognition
department (cz) RO
department 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*0101165
name1 Mikeš
name2 Stanislav
full_dept (cz) Rozpoznávání obrazu
full_dept Department of Pattern Recognition
department (cz) RO
department 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*0230019
name1 Hora
name2 Jan
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.
author
ARLID cav_un_auth*0101182
name1 Pudil
name2 Pavel
full_dept (cz) Rozpoznávání obrazu
full_dept Department of Pattern Recognition
department (cz) RO
department 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*0101239
name1 Žid
name2 Pavel
full_dept (cz) Rozpoznávání obrazu
full_dept Department of Pattern Recognition
department (cz) RO
department RO
institution UTIA-B
full_dept Department of Pattern Recognition
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://fst.utia.cz/download/FST3_Introduction_UTIA_TR2287.pdf
cas_special
project
project_id 1M0572
agency GA MŠk
ARLID cav_un_auth*0001814
project
project_id 2C06019
agency GA MŠk
country CZ
ARLID cav_un_auth*0216518
research CEZ:AV0Z10750506
abstract (eng) We introduce a new standalone widely applicable software library for feature selection (also known as attribute or variable selection), capable of reducing problem dimensionality to maximize the accuracy of data models, performance of automatic decision rules as well as to reduce data acquisition cost. The library can be exploited by users in research as well as in industry. Less experienced users can experiment with different provided methods and their application to real-life problems, experts can implement their own criteria or search schemes taking advantage of the toolbox framework. In this paper we first provide a concise survey of a variety of existing feature selection approaches. Then we focus on a selected group of methods of good general performance as well as on tools surpassing the limits of existing libraries. We build a feature selection framework around them and design an object-based generic software library. We describe the key design points and properties of the library.
reportyear 2011
RIV BD
mrcbC52 4 O 4o 20231122134453.6
permalink http://hdl.handle.net/11104/0195589
arlyear 2010
mrcbTft \nSoubory v repozitáři: 0357271.pdf
mrcbU10 2010
mrcbU10 Praha ÚTIA