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Ruta, Dymitr; Gabrys, Bogdan (2001)
Publisher: ICSC-NAISO Academic Press
Languages: English
Types: Unknown
Subjects: aintel, csi
Combining classifiers by majority voting (MV) has\ud recently emerged as an effective way of improving\ud performance of individual classifiers. However, the\ud usefulness of applying MV is not always observed and\ud is subject to distribution of classification outputs in a\ud multiple classifier system (MCS). Evaluation of MV\ud errors (MVE) for all combinations of classifiers in MCS\ud is a complex process of exponential complexity.\ud Reduction of this complexity can be achieved provided\ud the explicit relationship between MVE and any other\ud less complex function operating on classifier outputs is\ud found. Diversity measures operating on binary\ud classification outputs (correct/incorrect) are studied in\ud this paper as potential candidates for such functions.\ud Their correlation with MVE, interpreted as the quality\ud of a measure, is thoroughly investigated using artificial\ud and real-world datasets. Moreover, we propose new\ud diversity measure efficiently exploiting information\ud coming from the whole MCS, rather than its part, for\ud which it is applied.
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