| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0341554 |
| utime |
20240111140738.7 |
| mtime |
20100325235959.9 |
| title
(primary) (eng) |
Improving Sequential Feature Selection Methods Performance by Means of Hybridization |
| specification |
| page_count |
10 s. |
| media_type |
www |
|
| serial |
| ARLID |
cav_un_epca*0341553 |
| ISBN |
978-0-88986-830-4 |
| title
|
Proc. 6th IASTED Int. Conf. on Advances in Computer Science and Engineering |
| publisher |
| place |
Calgary |
| name |
ACTA Press |
| year |
2010 |
|
| editor |
|
|
| keyword |
Feature selection |
| keyword |
sequential search |
| keyword |
hybrid methods |
| keyword |
classification performance |
| keyword |
subset search |
| keyword |
statistical 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*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. |
|
| 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. |
|
| source |
|
| 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 |
|
| 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) |
In this paper we propose the general scheme of defining hybrid feature selection algorithms based on standard sequential search with the aim to improve feature selection performance, especially on high-dimensional or large-sample data. We show experimentally that “hybridization” has not only the potential to dramatically reduce FS search time, but in some cases also to actually improve classifier generalization, i.e., its classification performance on previously unknown data. |
| action |
| ARLID |
cav_un_auth*0261288 |
| name |
Advances in Computer Science and Engineering |
| place |
Sharm El Sheikh |
| dates |
15.03.2010-17.03.2010 |
| country |
EG |
|
| reportyear |
2011 |
| RIV |
BD |
| permalink |
http://hdl.handle.net/11104/0184495 |
| arlyear |
2010 |
| mrcbU56 |
PDF 942kb |
| mrcbU63 |
cav_un_epca*0341553 Proc. 6th IASTED Int. Conf. on Advances in Computer Science and Engineering 978-0-88986-830-4 689-1-689-10 Proc. 6th IASTED Int. Conf. on Advances in Computer Science and Engineering Calgary ACTA Press 2010 |
| mrcbU67 |
Rafea 340 |
|