@InProceedings{FariaSanSarRocTor:2013:WhFeMo,
author = "Faria, Fabio Augusto and Santos, Jefersson Alex dos and Sarkar,
Sudeep and Rocha, Anderson and Torres, Ricardo da Silva",
affiliation = "{University of Campinas} and {University of Campinas} and
{University of South Florida} and {University of Campinas} and
{University of Campinas}",
title = "Classifier Selection based on the Correlation of Diversity
Measures: When Fewer is More",
booktitle = "Proceedings...",
year = "2013",
editor = "Boyer, Kim and Hirata, Nina and Nedel, Luciana and Silva,
Claudio",
organization = "Conference on Graphics, Patterns and Images, 26. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "multiple classifier system, ensemble of classifiers, diversity
measures, coffee crop recognition.",
abstract = "The ever-growing access to high-resolution images has prompted the
development of region-based classification methods for remote
sensing images. However, in agricultural applications, the
recognition of specific regions is still a challenge as there
could be many different spectral patterns in a same studied area.
In this context, depending on the features used, different
learning methods can be used to create complementary classifiers.
Many researchers have developed solutions based on the use of
machine learning techniques to address these problems. Examples of
successful initiatives are those dedicated to the development of
learning techniques for data fusion or Multiple Classifier Systems
(MCS). In MCS, diversity becomes an essential factor for their
success. Different works have been using diversity measures to
select appropriate high-performance classifiers, but the challenge
of finding the optimal number of classifiers for a target task has
not been properly addressed yet. In general, the proposed
solutions rely on the a priori use of ad hoc strategies for
selecting classifiers, followed by the evaluation of their
effectiveness results during training. Searching by the optimal
number of classifiers, however, makes the selection process more
expensive. In this paper, we address this issue by proposing a
novel strategy for selecting classifiers to be combined based on
the correlation of different diversity measures. Diversity
measures are used to rank pairs of classifiers and the agreement
among ranked lists guides the classifier selection process. A
fusion framework has been used in our experiments targeted to the
classification of coffee crops in remote sensing images.
Experiment results demonstrate that the novel strategy is able to
yield comparable effectiveness results when contrasted to several
baselines, but using much fewer classifiers.",
conference-location = "Arequipa, Peru",
conference-year = "5-8 Aug. 2013",
doi = "10.1109/SIBGRAPI.2013.12",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2013.12",
language = "en",
ibi = "8JMKD3MGPBW34M/3EDGEL2",
url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3EDGEL2",
targetfile = "sibgrapi-2013-camera-ready-paper-114613.pdf",
urlaccessdate = "2024, May 03"
}