Machine Learning and Data Mining in Pattern Recognition: 9th International Conference, MLDM 2013, New York, NY, USA, July 19-25, 2013. Proceedings

671 Pages·2013·19.275 MB·English

by  Taku Onodera, Tetsuo Shibuya (auth.), Petra Perner (eds.)

Petra Perner (Ed.)
Machine Learning
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and Data Mining
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9
7

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A in Pattern Recognition
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9th International Conference, MLDM 2013
New York, NY, USA, July 2013
Proceedings
123
Lecture Notes in Artificial Intelligence 7988
Subseries of Lecture Notes in Computer Science
LNAISeriesEditors
RandyGoebel
UniversityofAlberta,Edmonton,Canada
YuzuruTanaka
HokkaidoUniversity,Sapporo,Japan
WolfgangWahlster
DFKIandSaarlandUniversity,Saarbrücken,Germany
LNAIFoundingSeriesEditor
JoergSiekmann
DFKIandSaarlandUniversity,Saarbrücken,Germany
Petra Perner (Ed.)
Machine Learning
and Data Mining
in Pattern Recognition
9th International Conference, MLDM 2013
NewYork, NY, USA, July 19-25, 2013
Proceedings
1 3
VolumeEditor
PetraPerner
InstituteofComputerVisionandAppliedComputerSciences,IBaI
Kohlenstr.2,04107Leipzig,Germany
E-mail:[email protected]
ISSN0302-9743 e-ISSN1611-3349
ISBN978-3-642-39711-0 e-ISBN978-3-642-39712-7
DOI10.1007/978-3-642-39712-7
SpringerHeidelbergDordrechtLondonNewYork
LibraryofCongressControlNumber:2013943122
CRSubjectClassification(1998):I.2.6,H.2.8,I.5,F.2,I.4,F.4,H.3
LNCSSublibrary:SL7–ArtificialIntelligence
©Springer-VerlagBerlinHeidelberg2013
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Preface
The nigth eventof the InternationalConference onMachine Learning andData
MiningMLDMwasheldinNewYork(www.mldm.de)runningundertheumbrella
of the World Congress “The Frontiers in Intelligent Data and Signal Analysis,
DSA2013” (www.worldcongressdsa.com).
Forthisedition,theProgramCommitteereceived212submissions.Afterthe
peer-review process, we accepted 60 high-quality papers for oral presentation;
fromthese51areincludedinthisproceedingsbook.Thesubjectsrangefromthe-
oretical topics on classification, clustering, association rule and pattern mining
to specific data mining methods for the different multimedia data types such as
imagemining, textmining, videomining andWebmining. Extendedversionsof
selectedpaperswill appearinthe International Journal of Transactions on Ma-
chine Learning and Data Mining (www.ibai-publishing.org/journal/mldm).
Twenty papers were selected for poster presentations and are published in
the MLDM Poster Proceedings by ibai-publishing (www.ibai-publishing.org).
AtutorialonDataMining,atutorialonCase-BasedReasoning,atutorialon
Intelligent Image Interpreation and Computer Vision in Medicine, Biotechnol-
ogy,Chemistry and FoodIndustry, a tutorial onStandardizationin Immunoflu-
orescence, and a tutorial on Big Data and Text Analysis were held before the
conference.
We were pleased to give out the best paper award for MLDM for the fourth
time this year (www.mldm.de). The final decision was made by the Best Paper
Award Committee based on the presentation by the authors and the discussion
with the auditorium. The ceremony took place at the end of the conference.
This prize is sponsored by ibai solutions (www.ibai-solutions.de),one of the
leading companies in data mining for marketing, Web mining, and e-commerce.
The conference was completed by an outlook on new challenging topics in
machine learning and data mining before the Best Paper Award Ceremony.
We would like to thank all reviewers for their highly professional work and
theireffortinreviewingthepapers.WealsothankthemembersoftheInstituteof
Applied Computer Sciences, Leipzig, Germany (www.ibai-institut.de), who
handedthe conferenceassecretariat.We appreciatethe helpandunderstanding
of the editorial staff at Springer Verlag, and in particular Alfred Hofmann, who
supported the publication of these proceedings in the LNAI series.
VI Preface
Last, but not least, we wish to thank all the speakers and participants
who contributed to the success of the conference. The next World Congress
(www.worldcongressdsa.com), “The Frontiers in Intelligent Data and Signal
Analysis,DSA2014,”willbeheldinSt.Petersburgcombiningthefollowingthree
events: International Conferences Machine Learning and Data Mining MLDM,
the Industrial ConferenceonData Mining ICDM, andthe InternationalConfer-
ence on Mass Data Analysis of Signals and Images in Medicine, Biotechnology,
Chemistry and Food Industry MDA.
July 2012 Petra Perner
International Conference on Machine Learning
and Data Mining, MLDM 2012
Chair
Petra Perner IBaI Leipzig, Germany
Committee
Agnar Aamodt NTNU, Norway
Jacky Baltes University of Manitoba, Canada
Christoph F. Eick University of Houston, USA
Ana Fred Technical University of Lisbon, Portugal
Giorgio Giacinto University of Cagliari, Italy
Makato Haraguchi Hokkaido University Sapporo, Japan
Robert J. Hilderman University of Regina, Canada
Eyke Hu¨llermeier University of Marburg, Germany
Atsushi Imiya Chiba University, Japan
Abraham Kandel University of South Florida, USA
Dimitrios A. Karras Chalkis Institute of Technology, Greece
Adam Krzyzak Concordia University, Montreal, Canada
Brian Lovell University of Queensland, Australia
Mariofanna Milanova University of Arkansas at Little Rock,USA
Thang V. Pham University of Amsterdam, The Netherlands
Maria da Graca Pimentel Universidade de Sao Paulo, Brazil
Petia Radeva Universitat Autonoma de Barcelona, Spain
Michael Richter University of Calgary, Canada
Fabio Roli University of Cagliari, Italy
Linda Shapiro University of Washington, USA
Sameer Singh LoughboroughUniversity, UK
Harald Steck Bell Laboratoris, USA
Francesco Tortorella Universit`a degli Studi di Cassino, Italy
Patrick Wang Northeastern University, USA
Additional Reviewers
P˚al Sætrom (Paal Saetrom) NTNU, Norway
Gleb Sizov NTNU, Norway
Theoharis Theoharis NTNU, Norway
Luigi Atzori University of Cagliari, Italy
Davide Ariu University of Cagliari, Italy
Giuliano Armano University of Cagliari, Italy
VIII InternationalConferenceonMachineLearningandDataMining,MLDM2012
Battista Biggio University of Cagliari, Italy
Igino Corona University of Cagliari, Italy
Luca Didaci University of Cagliari, Italy
Giorgio Fumera University of Cagliari, Italy
Danilo Pani University of Cagliari, Italy
Ignazio Pillai University of Cagliari, Italy
Luca Piras University of Cagliari, Italy
Riccardo Satta University of Cagliari, Italy
Roberto Tronci University of Cagliari, Italy
Eloisa Vargiu Barcelona Digital Centre of Technolgy, Spain
Table of Contents
The Gapped Spectrum Kernel for Support Vector Machines ........... 1
Taku Onodera and Tetsuo Shibuya
TyphoonDamage Scale Forecastingwith Self-OrganizingMaps Trained
by Selective Presentation Learning ................................. 16
Kazuhiro Kohara and Isao Sugiyama
Dynamic-Radius Species-Conserving Genetic Algorithm
for the Financial Forecasting of Dow Jones Index Stocks .............. 27
Michael Scott Brown, Michael J. Pelosi, and Henry Dirska
Multi Model Transfer Learning with RULES Family.................. 42
Hebah ElGibreen and Mehmet Sabih Aksoy
3D Geovisualisation Techniques Applied in Spatial Data Mining ....... 57
Carlos Roberto Valˆencio, Thatiane Kawabata,
Camila Alves de Medeiros, Rog´eria Cristiane Grat˜ao de Souza, and
Jos´e M´arcio Machado
Improving the Efficiency of Distributed Data Mining Using
an Adjustment Work Flow ........................................ 69
Jie Gao and Jo¨rg Denzinger
Sign Language Recognition with Support Vector Machines and Hidden
Conditional Random Fields: Going from Fingerspelling to Natural
Articulated Words ............................................... 84
C´esar Roberto de Souza and Ednaldo Brigante Pizzolato
Classification and Outlier Detection Based on Topic Based Pattern
Synthesis ....................................................... 99
Samrat Kokkula and Narasimha Murty Musti
DecrementalLearningofEvolvingFuzzyInferenceSystems:Application
to Handwritten Gesture Recognition ............................... 115
Manuel Bouillon, Eric Anquetil, and Abdullah Almaksour
Unsupervised Tagging of Spanish Lyrics Dataset Using Clustering...... 130
Fabio Leonardo Parra and Elizabeth Le´on
Feature Learning for Detection and Prediction of Freezing of Gait
in Parkinson’s Disease ............................................ 144
Sinziana Mazilu, Alberto Calatroni, Eran Gazit, Daniel Roggen,
Jeffrey M. Hausdorff, and Gerhard Tro¨ster
X Table of Contents
Multi-document Text Summarization Using Topic Model and Fuzzy
Logic........................................................... 159
Sanghoon Lee, Saeid Belkasim, and Yanqing Zhang
A Pattern Based Two-Stage Text Classifier.......................... 169
Moch Arif Bijaksana, Yuefeng Li, and Abdulmohsen Algarni
Applying a Lightweight Iterative Merging Chinese Segmentation
in Web Image Annotation......................................... 183
Chuen-Min Huang and Yen-Jia Chang
Relevance as a Metric for Evaluating Machine Learning Algorithms .... 195
Aravind Kota Gopalakrishna, Tanir Ozcelebi, Antonio Liotta, and
Johan J. Lukkien
Preceding Rule Induction with Instance Reduction Methods........... 209
Osama M. Othman and Christopher H. Bryant
Analytic Feature Selection for Support Vector Machines .............. 219
Carly Stambaugh, Hui Yang, and Felix Breuer
Evaluation of Hyperspectral Image Classification Using Random
Forest and Fukunaga-Koontz Transform ............................ 234
Semih Din¸c and Ramazan Savas Aygu¨n
SOM++: Integration of Self-Organizing Map and K-Means++
Algorithms...................................................... 246
Yunus Dogan, Derya Birant, and Alp Kut
Satellite Image Mining for Census Collection: A Comparative Study
with Respect to the Ethiopian Hinterland ........................... 260
Kwankamon Dittakan, Frans Coenen, and Rob Christley
Density Ratio Estimation in Support Vector Machine for Better
Generalization: Study on Direct Marketing Prediction ................ 275
Muhammad Syafiq Mohd Pozi, Aida Mustapha, and Anas Daud
Pacc - A Discriminative and Accuracy Correlated Measure
for Assessment of Classification Results ............................. 281
Madhav Sigdel and Ramazan Savas Aygu¨n
A Single-Domain, Representation-Learning Model for Big Data
Classification of Network Intrusion ................................. 296
Shan Suthaharan
Relation Decomposition: The Theory ............................... 311
Robert Martin Haralick, Ligon Liu, and Evan Misshula
Using Turning Point Detection to Obtain Better Regression Trees...... 325
Paul K. Amalaman, Christoph F. Eick, and Nouhad Rizk