| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0365937 |
| utime |
20240111140802.2 |
| mtime |
20111101235959.9 |
| WOS |
000298615100102 |
| DOI |
10.1109/ICSMC.2011.6083733 |
| title
(primary) (eng) |
Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition |
| specification |
| page_count |
8 s. |
| media_type |
CD-ROM |
|
| serial |
| ARLID |
cav_un_epca*0365984 |
| ISBN |
978-1-4577-0653-0 |
| title
|
Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2011) |
| page_num |
502-509 |
| publisher |
| place |
Piscataway |
| name |
IEEE |
| year |
2011 |
|
|
| keyword |
feature selection |
| keyword |
high dimensionality |
| keyword |
ranking |
| keyword |
classification |
| keyword |
machine learning |
| 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*0101091 |
| name1 |
Grim |
| name2 |
Jiří |
| 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*0021092 |
| name1 |
Pudil |
| name2 |
P. |
| country |
CZ |
|
| 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 |
|
| research |
CEZ:AV0Z10750506 |
| abstract
(eng) |
The paper addresses the problem of making dependency-aware feature selection feasible in pattern recognition problems of very high dimensionality. The idea of individually best ranking is generalized to evaluate the contextual quality of each feature in a series of randomly generated feature subsets. Each random subset is evaluated by a criterion function of arbitrary choice (permitting functions of high complexity). Eventually, the novel dependency-aware feature rank is computed, expressing the average benefit of including a feature into feature subsets. The method is efficient and generalizes well especially in very-high-dimensional problems, where traditional context-aware feature selection methods fail due to prohibitive computational complexity or to over-fitting. The method is shown well capable of over-performing the commonly applied individual ranking which ignores important contextual information contained in data. |
| action |
| ARLID |
cav_un_auth*0275416 |
| name |
The 2011 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2011) |
| place |
Anchorage, Alaska |
| dates |
09.10.2011-12.10.2011 |
| country |
US |
|
| reportyear |
2012 |
| RIV |
IN |
| permalink |
http://hdl.handle.net/11104/0201063 |
| mrcbC86 |
n.a. Proceedings Paper Computer Science Artificial Intelligence|Computer Science Cybernetics|Computer Science Information Systems |
| arlyear |
2011 |
| mrcbU34 |
000298615100102 WOS |
| mrcbU56 |
pdf |
| mrcbU63 |
cav_un_epca*0365984 Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2011) 978-1-4577-0653-0 502 509 Piscataway IEEE 2011 |
|