Building Classifier Ensembles Using Greedy Graph Edit Distance

dc.accessRightsAnonymous
dc.audienceScience
dc.contributor.authorRiesen, Kaspar
dc.contributor.authorFerrer, Miquel
dc.contributor.authorFischer, Andreas
dc.date.accessioned2015-10-08T09:29:11Z
dc.date.available2015-10-08T09:29:11Z
dc.date.issued2015
dc.description.abstractClassifier ensembles aim at more accurate classifications than single classifiers. In the present paper we introduce a general approach to building structural classifier ensembles, i.e. classifiers that make use of graphs as representation formalism. The proposed methodology is based on a recent graph edit distance approximation. The major observation that motivates the use of this particular approximation is that the resulting distances crucially depend on the order of the nodes of the underlying graphs. Our novel methodology randomly permutes the node order N times such that the procedure leads to N different distance approximations. Next, a distance based classifier is trained for each approximation and the results of the individual classifiers are combined in an appropriate way. In several experimental evaluations we make investigations on the classification accuracy of the resulting classifier ensemble and compare it with two single classifier systems.
dc.identifier.isbn978-3-319-20247-1
dc.identifier.urihttp://hdl.handle.net/11654/10146
dc.language.isode_CH
dc.publisherSpringer
dc.relation.ispartofMultiple Classifier Systems - 12th International Workshop, MCS 2015, Günzburg, Germany, June 29 - July 1, 2015
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.spatialHamburg
dc.titleBuilding Classifier Ensembles Using Greedy Graph Edit Distance
dc.type04B - Beitrag Konferenzschrift
dspace.entity.typePublication
fhnw.InventedHereYes
fhnw.IsStudentsWorkno
fhnw.PublishedSwitzerlandNo
fhnw.ReviewTypeAnonymous ex ante peer review of a complete publication
fhnw.affiliation.hochschuleHochschule für Wirtschaft FHNWde_CH
fhnw.affiliation.institutInstitut für Wirtschaftsinformatikde_CH
fhnw.pagination125-134
fhnw.publicationStatePublished
fhnw.seriesNumber9132
relation.isAuthorOfPublicationd761e073-1612-4d22-8521-65c01c19f97a
relation.isAuthorOfPublication811911d3-cfcd-4bb7-b1e4-aff33145b586
relation.isAuthorOfPublication.latestForDiscoveryd761e073-1612-4d22-8521-65c01c19f97a
Dateien

Lizenzbündel

Gerade angezeigt 1 - 1 von 1
Kein Vorschaubild vorhanden
Name:
license.txt
Größe:
2.94 KB
Format:
Item-specific license agreed upon to submission
Beschreibung: