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A dynamic model of classifier competence based on the local fuzzy confusion matrix and the random reference classifier

Pawel Trajdos; Marek Kurzynski

International Journal of Applied Mathematics and Computer Science (2016)

  • Volume: 26, Issue: 1, page 175-189
  • ISSN: 1641-876X

Abstract

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Nowadays, multiclassifier systems (MCSs) are being widely applied in various machine learning problems and in many different domains. Over the last two decades, a variety of ensemble systems have been developed, but there is still room for improvement. This paper focuses on developing competence and interclass cross-competence measures which can be applied as a method for classifiers combination. The cross-competence measure allows an ensemble to harness pieces of information obtained from incompetent classifiers instead of removing them from the ensemble. The cross-competence measure originally determined on the basis of a validation set (static mode) can be further easily updated using additional feedback information on correct/incorrect classification during the recognition process (dynamic mode). The analysis of computational and storage complexity of the proposed method is presented. The performance of the MCS with the proposed cross-competence function was experimentally compared against five reference MCSs and one reference MCS for static and dynamic modes, respectively. Results for the static mode show that the proposed technique is comparable with the reference methods in terms of classification accuracy. For the dynamic mode, the system developed achieves the highest classification accuracy, demonstrating the potential of the MCS for practical applications when feedback information is available.

How to cite

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Pawel Trajdos, and Marek Kurzynski. "A dynamic model of classifier competence based on the local fuzzy confusion matrix and the random reference classifier." International Journal of Applied Mathematics and Computer Science 26.1 (2016): 175-189. <http://eudml.org/doc/276588>.

@article{PawelTrajdos2016,
abstract = {Nowadays, multiclassifier systems (MCSs) are being widely applied in various machine learning problems and in many different domains. Over the last two decades, a variety of ensemble systems have been developed, but there is still room for improvement. This paper focuses on developing competence and interclass cross-competence measures which can be applied as a method for classifiers combination. The cross-competence measure allows an ensemble to harness pieces of information obtained from incompetent classifiers instead of removing them from the ensemble. The cross-competence measure originally determined on the basis of a validation set (static mode) can be further easily updated using additional feedback information on correct/incorrect classification during the recognition process (dynamic mode). The analysis of computational and storage complexity of the proposed method is presented. The performance of the MCS with the proposed cross-competence function was experimentally compared against five reference MCSs and one reference MCS for static and dynamic modes, respectively. Results for the static mode show that the proposed technique is comparable with the reference methods in terms of classification accuracy. For the dynamic mode, the system developed achieves the highest classification accuracy, demonstrating the potential of the MCS for practical applications when feedback information is available.},
author = {Pawel Trajdos, Marek Kurzynski},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {multiclassifier; cross-competence measure; confusion matrix; feedback information},
language = {eng},
number = {1},
pages = {175-189},
title = {A dynamic model of classifier competence based on the local fuzzy confusion matrix and the random reference classifier},
url = {http://eudml.org/doc/276588},
volume = {26},
year = {2016},
}

TY - JOUR
AU - Pawel Trajdos
AU - Marek Kurzynski
TI - A dynamic model of classifier competence based on the local fuzzy confusion matrix and the random reference classifier
JO - International Journal of Applied Mathematics and Computer Science
PY - 2016
VL - 26
IS - 1
SP - 175
EP - 189
AB - Nowadays, multiclassifier systems (MCSs) are being widely applied in various machine learning problems and in many different domains. Over the last two decades, a variety of ensemble systems have been developed, but there is still room for improvement. This paper focuses on developing competence and interclass cross-competence measures which can be applied as a method for classifiers combination. The cross-competence measure allows an ensemble to harness pieces of information obtained from incompetent classifiers instead of removing them from the ensemble. The cross-competence measure originally determined on the basis of a validation set (static mode) can be further easily updated using additional feedback information on correct/incorrect classification during the recognition process (dynamic mode). The analysis of computational and storage complexity of the proposed method is presented. The performance of the MCS with the proposed cross-competence function was experimentally compared against five reference MCSs and one reference MCS for static and dynamic modes, respectively. Results for the static mode show that the proposed technique is comparable with the reference methods in terms of classification accuracy. For the dynamic mode, the system developed achieves the highest classification accuracy, demonstrating the potential of the MCS for practical applications when feedback information is available.
LA - eng
KW - multiclassifier; cross-competence measure; confusion matrix; feedback information
UR - http://eudml.org/doc/276588
ER -

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