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dc.contributor.authorLysiak, R.
dc.contributor.authorKurzynski, M.
dc.contributor.authorWoloszynski, Tomasz
dc.identifier.citationLysiak, R. and Kurzynski, M. and Woloszynski, T. 2014. Optimal selection of ensemble classifiers using measures of competence and diversity of base classifiers. Neurocomputing. 126: pp. 29-35.

In this paper, a new probabilistic model using measures of classifier competence and diversity is proposed. The multiple classifier system (MCS) based on the dynamic ensemble selection scheme was constructed using both developed measures. Two different optimization problems of ensemble selection are defined and a solution based on the simulated annealing algorithm is presented. The influence of minimum value of competence and diversity in the ensemble on classification performance was investigated. The effectiveness of the proposed dynamic selection methods and the influence of both measures were tested using seven databases taken from the UCI Machine Learning Repository and the StatLib statistical dataset. Two types of ensembles were used: homogeneous or heterogeneous. The results show that the use of diversity positively affects the quality of classification. In addition, cases have been identified in which the use of this measure has the greatest impact on quality.

dc.publisherElsevier BV
dc.titleOptimal selection of ensemble classifiers using measures of competence and diversity of base classifiers
dc.typeJournal Article
curtin.accessStatusFulltext not available

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