Data-Fusion Techniques for Open-set Recognition Problems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Cordova-Neira:2018:ieeeAccess,
-
author = "Manuel A. {Cordova Neira} and
Pedro R. {Mendes Junior} and Anderson Rocha and Ricardo {da S. Torres}",
-
journal = "IEEE Access",
-
title = "Data-Fusion Techniques for Open-set Recognition
Problems",
-
year = "2018",
-
keywords = "genetic algorithms, genetic programming, Pattern
recognition, Open-set Recognition, Data Fusion,
Optimum-Path Forest, Majority Voting",
-
DOI = "doi:10.1109/ACCESS.2018.2824240",
-
abstract = "Most pattern classification techniques are focused on
solving closed-set problems - in which a classifier is
trained with samples of all classes that may appear
during the testing phase. In many situations, however,
samples of unknown classes, i.e., whose classes did not
have any example during the training stage, need to be
properly handled during testing. This specific set up
is referred to in the literature as open-set
recognition. Open-set problems are harder as they might
be ill-sampled, not sampled at all, or even undefined.
Differently from existing literature, here, we aim at
solving open-set recognition problems combining
different classifiers and features while, at the same
time, taking care of unknown classes. Researchers have
greatly benefited from combining different methods in
order to achieve more robust and reliable classifiers
in daring recognition conditions, but those solutions
have often focused on closed-set set ups. In this work,
we propose the integration of a newly designed open set
graph-based Optimum-Path Forest (OSOPF) classifier with
Genetic Programming (GP) and Majority Voting fusion
techniques. While OSOPF takes care of learning decision
boundaries more resilient to unknown classes and
outliers, the GP, combines different problem features
to discover appropriate similarity functions and allow
a more robust classification through early fusion.
Finally, the Majority-Voting approach combines
different classification evidence from different
classifier outcomes and features through late-fusion
techniques. Performed experiments show the proposed
data-fusion approaches yield effective results for
open-set recognition problems, significantly
outperforming existing counterparts in the literature
and paving the way for investigations in this field.",
-
notes = "Also known as \cite{8332940}",
- }
Genetic Programming entries for
Manuel Alberto Cordova Neira
Pedro Ribeiro Mendes Junior
Anderson de Rezende Rocha
Ricardo da Silva Torres
Citations