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Paper details
Number 1 - March 2016
Volume 26 - 2016
A dynamic model of classifier competence based on the local fuzzy confusion matrix and the random reference classifier
Pawel Trajdos, Marek Kurzynski
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.
Keywords
multiclassifier, cross-competence measure, confusion matrix, feedback information