Frontiers in Computational Intelligence PDF
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Studies in Computational Intelligence 739 Sanaz Mostaghim Andreas Nürnberger Christian Borgelt E ditors Frontiers in Computational Intelligence Studies in Computational Intelligence Volume 739 Series editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland e-mail: [email protected] The series “Studies in Computational Intelligence” (SCI) publishes new develop- mentsandadvancesinthevariousareasofcomputationalintelligence—quicklyand with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the worldwide distribution, which enable both wide and rapid dissemination of research output. More information about this series at http://www.springer.com/series/7092 ⋅ Sanaz Mostaghim Andreas Nürnberger Christian Borgelt Editors Frontiers in Computational Intelligence 123 Editors Sanaz Mostaghim Christian Borgelt Faculty of Computer Science Department ofComputer andInformation Otto vonGuericke University Magdeburg Science Magdeburg University of Konstanz Germany Konstanz Germany Andreas Nürnberger Faculty of Computer Science Otto vonGuericke University Magdeburg Magdeburg Germany ISSN 1860-949X ISSN 1860-9503 (electronic) Studies in Computational Intelligence ISBN978-3-319-67788-0 ISBN978-3-319-67789-7 (eBook) https://doi.org/10.1007/978-3-319-67789-7 LibraryofCongressControlNumber:2017953812 ©SpringerInternationalPublishingAG2018 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar methodologynowknownorhereafterdeveloped. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfrom therelevantprotectivelawsandregulationsandthereforefreeforgeneraluse. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authorsortheeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinor for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictionalclaimsinpublishedmapsandinstitutionalaffiliations. Printedonacid-freepaper ThisSpringerimprintispublishedbySpringerNature TheregisteredcompanyisSpringerInternationalPublishingAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface Since the middle of the twentieth century, and accelerating sharply around the 1980s, computer science developed an area in which it is tried to equip computers with the capability to exhibit intelligent behavior, and thus to mimic the ability of humans and other intelligent animals to deal with uncertainty and vagueness, to learn from experience, and to adapt to changing environments. This area has aptly been named “Computational Intelligence” and nowadays belongs to the most actively researched areas not only in computer science, but even in the whole of science, technology and engineering. Rudolf Kruse, towhomthisbookisdedicatedontheoccasionofhisretirement from his post as a Professor at the Otto-von-Guericke-University of Magdeburg, Germany, helped substantially in shaping this area, not only with his own signif- icant contributions, which are manifold, but also, and possibly even more, by promotingthisarea,byencouragingmanyotherstoenterit,andbysupportingthem to advance its state of the art. This book collects several contributions that honor the achievements of Rudolf Kruseeitherdirectlyorindirectly,byreferringexplicitlytohisworkorbyshowing thestate oftheart inspecificareas ofComputational Intelligence on which hehad an influence. The main focus of these contributions lies on treating vague data as wellasuncertainandimpreciseinformationwithautomatedprocedures,whichuse techniques from statistics, control theory, clustering, neural networks, etc., to extract useful and employable knowledge. The contribution by Enric Trillas and Rudolf Seising considers a problem that hasbeendiscussedinasometimesheatedfashionsincefuzzysetsenteredthestage of science, namely the interpretation and proper mathematical modeling of fuzzy sets.Theyremindusthatdespiteamultitudeofconceptswithdifferentnamesthere arenotmanydifferenttypesoffuzzysets,butrathermerelydifferentformalizations of the linguistic phenomena of vagueness that one tries to model with fuzzy sets. The contribution by Maria Ángeles Gil compares two definitions offuzzy ran- dom variables, one of which originated from a book Rudolf Kruse wrote together withKlaus-DieterMeyer(“StatisticswithVagueData”),whichiscontrastedtothe one by Puri and Ralescu. Again, we find an interpretation problem at the heart v vi Preface ofthedistinction:Arefuzzyphenomenaaspectsoftherealworldoraretheymerely aspectsofourobservationofit?Dependingontheanswer,slightlydifferentresults and procedures are obtained. The contribution by Inés Couso and Eyke Hüllermeier considers the case of statistically estimating ranking information from an incomplete observation of a ranking.Thissettingcanbemodeledalongthegenerallinesofhandlingset-valued information, similar to how set-valued information is treated in Dempster–Shafer theoryorpossibilitytheory,thelatterofwhichwasdevelopedoutoffuzzytheoryas an alternative calculus to model uncertainty, orthogonal to probability theory and with a different emphasis. The contribution by Thomas Runkler et al. takes a look at the area of fuzzy control,whichhascertainlybeenthemostsuccessfuloutcomeoffuzzytheoryw.r.t. actual applications. Again, the connection to Rudolf Kruse is very direct, as he played a decisive part in several applications offuzzy control at Volkswagen (idle speedcontrol,automaticgearshift,etc.).ThomasRunkleretal.studyhowatype-2 fuzzy setcanbedefuzzified,whichisthenecessarylaststepintheprocessingofa fuzzy controller based on type-2 fuzzy sets. ThecontributionbyFrankKlawonnenterstheareaofclustering,andespecially fuzzy clustering in its various forms, on which Rudolf Kruse coauthored an influential book (“Fuzzy Cluster Analysis”) with Frank Höppner, Frank Klawonn, andThomasRunkler.FrankKlawonnconsidersdynamicdataassigningassessment clustering, which was developed out of noise clustering, a fuzzy clustering approach to better deal with noise and outliers, to detect single clusters in an iterative fashion, and applies this approach to improve cluster detection in time-resolved data from the life sciences. ThecontributionbySaraMahallatietal.isalsolocatedintheareaofclustering and considers the task of interpreting the structure of a clustering result, especially withthehelpofthewell-knownDunnindex,butalsowithvisualassessmentbased on properly reordered distance matrices. The authors explore the close connection of both approaches to the time-tested single linkage hierarchicalclustering method and apply their theory to thespecific task of clustering waveform data, which, due to the time-dependent nature of the data, is closely connected to the preceding contribution. The contribution by Malte Oeljeklaus et al. deals with one of the currently hottesttopicsinComputationalIntelligence,namelydeeplearningneuralnetworks forimageanalysis.Withthediscoverythatthereachoftheuniversalapproximation theorem for neural networks is limited by the potentially needed huge size of a single hidden layer and the development of new activation functions and new training methods that allow, supported by advances in hardware, for efficient training ofneuralnetworks with manyhidden layers(“deep”neural networks),the areaofneuralnetworkshasseenunprecedentedsuccessesandconsequentlyasurge ofinterestinrecentyears.Thiscontributiondealswithanapplicationofsuchdeep learning neural networks to traffic scene segmentation and recognition, which is a decisive step toward enabling autonomously driving vehicles (“self-driving cars”). Preface vii ThecontributionbyChristerCarlssonlooksatthewidercontextandapplication potential offuzzy methods and, more generally, soft computing technology in the areaofmanagementscienceandoperationsresearchor,asitismoreoftenreferred to today, business analytics. Here, fuzzy ontologies may be used for capturing domain-specific semantics for information retrieval by using fuzzy concepts, rela- tions, and instances, and by defining and processing degrees of inclusion and coverage between concepts, which are then processed by a typical fuzzy min-max approach. However, since using software tools built with such sophisticated methodologyrequireshighexpertiseoftheoperators,digitalcoachesareneededto help domain experts to fully exploit the benefits of such systems, which Carlsson also considers and advocates. We are very grateful to all authors who accepted our invitation to contribute a chapter to this volume and to all reviewers who helped to improve the contribu- tions. Furthermore, we express our gratitude to Janusz Kacprzyk, who made it possible to publish this book in the Springer series “Studies in Computational Intelligence.” Last, but not least, we thank Springer-Verlag for the excellent col- laboration that helped a lot to publish this book in time. Magdeburg, Germany Sanaz Mostaghim Magdeburg, Germany Andreas Nürnberger Konstanz, Germany Christian Borgelt June 2017 Contents What a Fuzzy Set Is and What It Is not? . . . . . . . . . . . . . . . . . . . . . . . 1 Enric Trillas and Rudolf Seising Fuzzy Random Variables à la Kruse & Meyer and à la Puri & Ralescu: Key Differences and Coincidences . . . . . . . . . . . . . . . . 21 María Ángeles Gil Statistical Inference for Incomplete Ranking Data: A Comparison of Two Likelihood-Based Estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Inés Couso and Eyke Hüllermeier Interval Type–2 Defuzzification Using Uncertainty Weights. . . . . . . . . . 47 Thomas A. Runkler, Simon Coupland, Robert John and Chao Chen Exploring Time-Resolved Data for Patterns and Validating Single Clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Frank Klawonn Interpreting Cluster Structure in Waveform Data with Visual Assessment and Dunn’s Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Sara Mahallati, James C. Bezdek, Dheeraj Kumar, Milos R. Popovic and Taufik A. Valiante A Shared Encoder DNN for Integrated Recognition and Segmentation of Traffic Scenes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Malte Oeljeklaus, Frank Hoffmann and Torsten Bertram Fuzzy Ontology Support for Knowledge Mobilisation . . . . . . . . . . . . . . 121 Christer Carlsson ix What a Fuzzy Set Is and What It Is not? Enric Trillas and Rudolf Seising Abstract Although in the literature there appear ‘type-one’ fuzzy sets, ‘type-two’ fuzzy sets, ‘intuitionistic’ fuzzy sets, etc., this theoretically driven paper tries to argue that only one type offuzzy sets actually exists. This is due to the difference between the concepts of a fuzzy set” and a “membership function”. 1 Introduction Althoughintheliteraturethereappear ‘type-one’fuzzy sets,‘type-two’fuzzy sets, ‘intuitionistic’fuzzysets,etc.,thistheoreticallydrivenpapertriestoarguethatonly one type of fuzzy sets actually exists. This is due to the difference between the conceptsofafuzzyset”anda“membershipfunction”.Bothconceptsdeservetobe clarified. Fuzzysetscan,forinstance,becontextuallyspecifiedbyamembershipfunction with values in the real unit interval but, nevertheless, membership functions with valuesoutofthisintervalcanbe,insomesituations,significant,suitableanduseful. Situationsinwhicheithertherangeoftheirvaluescannotbepresumedtobetotally ordered, or it is impossible to precisely determine the membership numerical val- ues,orthelinearlyorderedrealunitintervalproducesadrasticsimplificationofthe meaning of the fuzzy set’s linguistic label by enlarging it through its ‘working’ meaning. Indeed,thispapernegatestheexistenceof‘otherfuzzysets’thanfuzzysets,but it shows the possible suitability of designing their membership functions for (*)ToProfessorRudolfKrusewiththegreatestesteem. E.Trillas UniversityofOviedo,Oviedo,Spain R.Seising(✉) Friedrich-SchillerUniversityJena,Jena,Germany e-mail:[email protected] ©SpringerInternationalPublishingAG2018 1 S.Mostaghimetal.(eds.),FrontiersinComputationalIntelligence,Studies inComputationalIntelligence739,https://doi.org/10.1007/978-3-319-67789-7_1
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