New measure of classifier dependency in multiple classifier systems

Dymitr Ruta, Bogdan Gabrys

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations


Recent findings in the domain of combining classifiers provide a surprising revision of the usefulness of diversity for modelling combined performance. Although there is a common agreement that a successful fusion system should be composed of accurate and diverse classifiers, experimental results show very weak correlations between various diversity measures and combining methods. Effectively neither the combined performance nor its improvement against mean classifier performance seem to be measurable in a consistent and well defined manner. At the same time the most successful diversity measures, barely regarded as measuring diversity, are based on measuring error coincidences and by doing so they move closer to the definitions of combined errors themselves. Following this trend we decided to use directly the combining error normalized within the derivable error limits as a measure of classifiers dependency. Taking into account its simplicity and representativeness we chose majority voting error for the construction of the measure. We examine this novel dependency measure for a number of real datasets and classifiers showing its ability to model combining improvements over an individual mean.

Original languageBritish English
Title of host publicationMultiple Classifier Systems - 3rd International Workshop, MCS 2002, Proceedings
EditorsFabio Roli, Josef Kittler
PublisherSpringer Verlag
Number of pages10
ISBN (Print)3540438181, 9783540438182
StatePublished - 2002
Event3rd International Workshop on Multiple Classifier Systems, MCS 2002 - Cagliari, Italy
Duration: 24 Jun 200226 Jun 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Workshop on Multiple Classifier Systems, MCS 2002


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