Ruta, D. and Gabrys, B., 2005. Classifier selection for majority voting. Information Fusion, 6 (1), pp. 63-81.
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Individual classification models are recently challenged by combined pattern recognition systems, which often show better performance. In such systems the optimal set of classifiers is first selected and then combined by a specific fusion method. For a small number of classifiers optimal ensembles can be found exhaustively, but the burden of exponential complexity of such search limits its practical applicability for larger systems. As a result, simpler search algorithms and/or selection criteria are needed to reduce the complexity. This work provides a revision of the classifier selection methodology and evaluates the practical applicability of diversity measures in the context of combining classifiers by majority voting. A number of search algorithms are proposed and adjusted to work properly with a number of selection criteria including majority voting error and various diversity measures. Extensive experiments carried out with 15 classifiers on 27 datasets indicate inappropriateness of diversity measures used as selection criteria in favour of the direct combiner error based search. Furthermore, the results prompted a novel design of multiple classifier systems in which selection and fusion are recurrently applied to a population of best combinations of classifiers rather than the individual best. The improvement of the generalisation performance of such system is demonstrated experimentally.
|Additional Information:||This paper is an example of our extensive research into multiple classifier systems (MCS). It provides a comprehensive analysis, first of this type and scale published anywhere, of a large number of diversity measures (16), data sets (27), classifiers (15) and searching methods (7) clarifying the role and usefulness of diversity in MCS. The second main original contribution is related to proposing a novel multistage selection-fusion (MSF) model which is an example of multilevel combination structures, introduced within our group, offering improved generalisation performance. Such multilevel combination structures have provided significant improvements in the highly competitive forecasting software for major European airline. The paper has been listed in the Top 25 Hottest Articles of Information Fusion journal from June 2004 until December 2005.|
|Subjects:||Generalities > Computer Science and Informatics > Artificial Intelligence|
Generalities > Computer Science and Informatics
|Group:||School of Design, Engineering & Computing > Smart Technology Research Centre|
|Deposited By:||INVALID USER|
|Deposited On:||17 Dec 2007|
|Last Modified:||07 Mar 2013 14:36|
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