Multiple Classifier Systems: 12th International Workshop, MCS 2015, Günzburg, Germany, June 29 - July 1, 2015, Proceedings

240 Pages·2015·11.383 MB·English

by  Friedhelm Schwenker, Fabio Roli, Josef Kittler (eds.)

Friedhelm Schwenker
Fabio Roli
Josef Kittler (Eds.)
2 Multiple
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Classifier Systems
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12th International Workshop, MCS 2015
Günzburg, Germany, June 29 – July 1, 2015
Proceedings
123
Lecture Notes in Computer Science 9132
Commenced Publication in 1973
Founding and Former Series Editors:
Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen
Editorial Board
David Hutchison
Lancaster University, Lancaster, UK
Takeo Kanade
Carnegie Mellon University, Pittsburgh, PA, USA
Josef Kittler
University of Surrey, Guildford, UK
Jon M. Kleinberg
Cornell University, Ithaca, NY, USA
Friedemann Mattern
ETH Zurich, Zürich, Switzerland
John C. Mitchell
Stanford University, Stanford, CA, USA
Moni Naor
Weizmann Institute of Science, Rehovot, Israel
C. Pandu Rangan
Indian Institute of Technology, Madras, India
Bernhard Steffen
TU Dortmund University, Dortmund, Germany
Demetri Terzopoulos
University of California, Los Angeles, CA, USA
Doug Tygar
University of California, Berkeley, CA, USA
Gerhard Weikum
Max Planck Institute for Informatics, Saarbrücken, Germany
More information about this series at http://www.springer.com/series/7412
Friedhelm Schwenker Fabio Roli
(cid:129)
Josef Kittler (Eds.)
Multiple
fi
Classi er Systems
12th International Workshop, MCS 2015
ü –
G nzburg, Germany, June 29 July 1, 2015
Proceedings
123
Editors
Friedhelm Schwenker Josef Kittler
Ulm University University of Surrey
Ulm Guildford
Germany UK
FabioRoli
University of Cagliari
Cagliari
Italy
ISSN 0302-9743 ISSN 1611-3349 (electronic)
Lecture Notesin Computer Science
ISBN 978-3-319-20247-1 ISBN978-3-319-20248-8 (eBook)
DOI 10.1007/978-3-319-20248-8
LibraryofCongressControlNumber:2015940974
LNCSSublibrary:SL6–ImageProcessing,ComputerVision,PatternRecognition,andGraphics
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Preface
Thisbookpresentstheproceedingsofthe12thIAPRWorkshoponMultipleClassifier
Systems (MCS 2015) held at Reisensburg Castle, the research center of Ulm Univer-
sity,Germany,duringJune29–July1,2015.TheseriesofMCSworkshopshasacted
asamajorforumforinternationalresearchersandpractitionersfromthecommunityof
multiple classifier systems in pattern recognition and machine learning.
TheProgramCommitteeofMCS2015selected19papersforthescientificprogram.
Two IAPR Invited Sessions given by Dr. George Cybenko, Darmouth College, USA,
and Dr. Marcello Pelillo, University of Venice, Italy, enriched the workshop.
MCS 2015 would not have been possible without the help and support of many
peopleandorganizations.Firstofall,wearegratefultoallauthorswhosubmittedtheir
work to MCS. We thank the members of the Program Committee and the additional
reviewersforperformingthedifficulttaskofselectingthebestpapersforpresentation,
and we hope that readers of this volume will enjoy it and be inspired from its
contributions.
MCS 2015 was supported by the International Association for Pattern Recognition
(IAPR), by the University of Cagliari, Italy, by the University of Surrey, UK, and by
UlmUniversity,Germany,whichhostedthisevent.Specialthankstothepeopleofthe
localorganization,MiriamSchmidt,MartinSchels,MichaelGlodek,MarkusKächele,
SaschaMeudt,andPatrickThiam.Finally,wewishtoexpressourgratitudetoSpringer
for publishing our proceedings in their LNCS series and for their constant support.
July 2015 Friedhelm Schwenker
Fabio Roli
Josef Kittler
Organization
Organizing Committee
Friedhelm Schwenker University of Ulm, Germany
Fabio Roli University of Cagliari, Italy
Josef Kittler University of Surrey, UK
Program Committee
Jon Benediktsson, Iceland Gonzalo Martinez-Munoz, Spain
Gavin Brown, UK Juan Jose Rodriguez, Spain
Cesare Furlanello, Italy Arun Ross, USA
Giorgio Fumera, Italy Carlo Sansone, Italy
Joydeep Ghosh, USA Giorgio Valentini, Italy
Larry Hall, USA Terry Windeatt, UK
Tin Kam Ho, USA Xin Yao, China
Philip Kegelmeyer, USA Yang Yu, China
Ludmila Kuncheva, UK Zhi-Hua Zhou, China
Sponsoring Institutions
University of Ulm, Germany
University of Cagliari, Italy
University of Surrey, UK
International Association for Pattern Recognition (IAPR)
Deep Learning of Behaviors
George Cybenko
Thayer Schoolof Engineering
Dartmouth College
Hanover NH03755, USA
[email protected]
Abstract. Deep learning has generated much research and commercialization
interest recently. In a way, it is the third incarnation of neural networks as
pattern classifiers, using insightful algorithms and architectures that act as
unsupervisedauto-encoders, learning hierarchies offeatures in adataset.
Afterashortreviewofthatwork,wewilldiscussthechallengesassociated
withtheanalysisofbehaviorsobservedastimeseriesofcategoricaldata.Novel
computational approaches for deep learning of behaviors as opposed to just
static patterns will be presented. Our approach is based on structured non-
negativematrixfactorizationsofmatricesthatencodeobservationfrequenciesof
behaviors.
These techniques can be used to robustly characterize and exploit diverse
behaviorsinsecurityapplicationssuchascovertchanneldetectionandcoding.
Examples ofsuchapplications will bepresented.
Relatedresultsabouttheroleofdiversityincomputersecurityapplications
willalsobeintroducedwhereinadversarialdynamicsdictatesthatattackersand
defenders coevolve. As a result, the use of multiple diverse detection and
mitigation techniques makes the attackers’effective workfactor much higher.
Similarity-Based Pattern Recognition:
A Game-Theoretic Perspective
Marcello Pelillo
Ca’Foscari University of Venice
30172Venezia Mestre, Italy
[email protected]
Abstract.Similarity-based methodsareemergingasapowerfultoolinpattern
recognition and machine learning because of their ability to overcome the
intrinsiclimitationsoftraditionalfeature-vectorapproaches.Bydepartingfrom
vectorspace representations, however, one is confronted with the challenging
problem of dealing with (dis)similarities that do not necessarily possess the
Euclideanbehaviorornotevenobeytherequirementsofametric.Inthistalk,
I will maintain that game theory offers an elegant and powerful conceptual
framework which serves well this purpose, and I will describe recent attempts
aimedatformulatingvarioussimilarity-basedpatternrecognitionproblemsfrom
agame-theoreticperspective.Particularemphasiswillbegiventoevolutionary-
based models which, in contrast to the classical theory, offer an intriguing
dynamicalsystemperspective.Finally,Iwilldescrivesomeapplicationsofthis
approach within the contextof multiple classifier systems.
Contents
Theory and Algorithms
A Novel Bagging Ensemble Approach for Variable Ranking and Selection
for Linear Regression Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Chun-Xia Zhang, Jiang-She Zhang, and Guan-Wei Wang
A Hierarchical Ensemble Method for DAG-Structured Taxonomies. . . . . . . . 15
Peter N. Robinson, Marco Frasca, Sebastian Köhler, Marco Notaro,
Matteo Re, and Giorgio Valentini
Diversity Measures and Margin Criteria in Multi-class Majority
Vote Ensemble. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Ayako Mikami, Mineichi Kudo, and Atsuyoshi Nakamura
Fractional Programming Weighted Decoding for Error-Correcting
Output Codes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Firat Ismailoglu, I.G. Sprinkhuizen-Kuyper, Evgueni Smirnov,
Sergio Escalera, and Ralf Peeters
Instance-Based Decompositions of Error Correcting Output Codes . . . . . . . . 51
Firat Ismailoglu, Evgueni Smirnov, Nikolay Nikolaev, and Ralf Peeters
Pruning Bagging Ensembles with Metalearning. . . . . . . . . . . . . . . . . . . . . . 64
Fábio Pinto, Carlos Soares, and João Mendes-Moreira
Multi-label Selective Ensemble. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
Nan Li, Yuan Jiang, and Zhi-Hua Zhou
Supervised Selective Combination of Diverse Object-Representation
Modalities for Regression Estimation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
Olga Krasotkina, Oleg Seredin, and Vadim Mottl
Detecting Ordinal Class Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
RaphaelLattke,LudwigLausser,ChristophMüssel,andHansA.Kestler
Calibrating AdaBoost for Asymmetric Learning . . . . . . . . . . . . . . . . . . . . . 112
Nikolaos Nikolaou and Gavin Brown
Building Classifier Ensembles Using Greedy Graph Edit Distance. . . . . . . . . 125
Kaspar Riesen, Miquel Ferrer, and Andreas Fischer