| bibtype |
V -
Research Report
|
| ARLID |
0357265 |
| utime |
20240103194938.1 |
| mtime |
20110404235959.9 |
| title
(primary) (eng) |
Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition Problems |
| publisher |
| place |
Praha |
| name |
ÚTIA AV ČR, v.v.i |
| pub_time |
2011 |
|
| specification |
|
| edition |
| name |
Research Report |
| volume_id |
2295 |
|
| keyword |
feature selection, |
| keyword |
high dimensionality |
| keyword |
ranking |
| keyword |
generalization |
| keyword |
over-fitting |
| keyword |
stability |
| keyword |
classification |
| keyword |
pattern recognition |
| 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. |
|
| source |
|
| cas_special |
| project |
| project_id |
1M0572 |
| agency |
GA MŠk |
| ARLID |
cav_un_auth*0001814 |
|
| project |
| project_id |
2C06019 |
| agency |
GA MŠk |
| 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. |
| reportyear |
2012 |
| RIV |
BD |
| mrcbC52 |
4 O 4o 20231122134453.4 |
| permalink |
http://hdl.handle.net/11104/0195583 |
| arlyear |
2011 |
| mrcbTft |
\nSoubory v repozitáři: 0357265.pdf |
| mrcbU10 |
2011 |
| mrcbU10 |
Praha ÚTIA AV ČR, v.v.i |
|