Granular Neural Networks, Pattern Recognition and Bioinformatics PDF
Preview Granular Neural Networks, Pattern Recognition and Bioinformatics
Studies in Computational Intelligence 712 Sankar K. Pal Shubhra S. Ray Avatharam Ganivada Granular Neural Networks, Pattern Recognition and Bioinformatics Studies in Computational Intelligence Volume 712 Series editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland e-mail: [email protected] About this Series 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 Sankar K. Pal Shubhra S. Ray (cid:129) Avatharam Ganivada Granular Neural Networks, Pattern Recognition and Bioinformatics 123 Sankar K.Pal Avatharam Ganivada Centerfor Soft Computing Research Centerfor Soft Computing Research Indian Statistical Institute Indian Statistical Institute Kolkata Kolkata India India Shubhra S.Ray Centerfor Soft Computing Research Indian Statistical Institute Kolkata India ISSN 1860-949X ISSN 1860-9503 (electronic) Studies in Computational Intelligence ISBN978-3-319-57113-3 ISBN978-3-319-57115-7 (eBook) DOI 10.1007/978-3-319-57115-7 LibraryofCongressControlNumber:2017937261 ©SpringerInternationalPublishingAG2017 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 To our parents Preface The volume “Granular Neural Networks, Pattern Recognition and Bioinformatics” isanoutcomeofthegranularcomputingresearchinitiatedin2005attheCenterfor Soft Computing Research: A National Facility, Indian Statistical Institute (ISI), Kolkata. The center was established in 2005 by the Department of Science and Technology, Govt. of India under its prestigious IRHPA (Intensification of Research in High Priority Area) program. Now it is an Affiliated Institute of ISI. Granulation is a process like self-production, self-organization, functioning of brain, Darwinian evolution, group behavior and morphogenesis—which are abstracted from natural phenomena. Accordingly, it has become a component of natural computing. Granulation is inherent in human thinking and reasoning pro- cess,andplaysanessentialroleinhumancognition.Granularcomputing(GrC)isa problem-solving paradigm dealing with the basic elements, called granules. Agranulemaybedefinedastheclumpofindistinguishableelementsthataredrawn together, for example, by indiscernibility, similarly, proximity or functionality. Granules with different levels of granularity, as determined by its size and shape, may represent a system differently. Since in GrC, computations are performed on granules,ratherthanonindividualdatapoints,computationtimeisgreatlyreduced. ThismadeGrCaveryusefulframeworkfor designing scalablepattern recognition and data mining algorithms for handling large data sets. Thetheoryofroughsetsthatdealswithaset(concept)definedoveragranulated domain provides an effective tool for extracting knowledge from databases. Two oftheimportant characteristics of thistheorythat drew theattentionof researchers inpatternrecognitionanddecisionscienceareitscapabilityofuncertaintyhandling andgranularcomputing.Whiletheconceptofgranularcomputingisinherentinthis theory where the granules are defined by equivalence relations, uncertainty arising from the indiscernibility in the universe of discourse can be handled using the concept of lower and upper approximations of the set. Lower and upper approxi- mate regions respectively denote the granules which definitely, and definitely and possiblybelongtotheset.Inreal-lifeproblemsthesetandgranules,eitherorboth, could be fuzzy; thereby resulting in fuzzy-lower and fuzzy-upper approximate regions, characterized by membership functions. vii viii Preface Granular neural networks described in the present book are pivoted on the characteristics of lower approximate regions of classes demonstrating its signifi- cance. The basic principle of design is—detect lower approximations of classes (regions where the class belonging of samples is certain); find class information granules, called knowledge; form basic networks based on those information, i.e., by knowledge encoding; and then grow the network with samples belonging to upperapproximateregions(i.e.,samplesofpossibleaswellasdefinitebelonging). Informationgranulesconsideredarefuzzytodealwithreal-lifeproblems.Theclass boundaries generated in this way provide optimum error rate. The networks thus developedarecapableofefficientandspeedylearningwithenhancedperformance. These systems have a strong promise to Big data analysis. The volume, consisting of seven chapters, provides a treatise in a unified framework in this regard, and describes how fuzzy rough granular neural network technologies can be judiciously formulated and used in building efficient pattern recognition and mining models. Formation of granules in the notion of both fuzzy and rough sets is stated. Judicious integration in forming fuzzy-rough information granules based on lower approximate regions enables the network in determining the exactness in class shape as well as handling the uncertainties arising from overlapping regions. Layered network and self-organizing map are considered as basic networks. Basedontheexistingaswellasnewresults,thebookisstructuredaccordingto themajorphasesofapatternrecognitionsystem(e.g.,classification,clustering,and feature selection) with a balanced mixture of theory, algorithm and application. Chapter 1 introduces granular computing, pattern recognition and data mining for the convenience of readers. Beginning with the concept of natural computing, the chapter describes in detail the various characteristics and facets of granular com- puting, granular information processing aspects of natural computing, its different components such as fuzzy sets, rough sets and artificial networks, relevance of granular neural networks, different integrated granular information processing systems, and finally the basic components of pattern recognition and data mining, andbigdataissues.Chapter2dealswithclassificationtask,Chaps.3and5address clustering problems, and Chap. 4 describes feature selection methodologies, all from the point of designing fuzzy rough granular neural network models. Special emphasis has been given to dealing with problems in bioinformatics, e.g., gene analysis and RNA secondary structure prediction, with a possible use of the granular computing paradigm. These are described in Chaps. 6 and 7 respectively. New indices for cluster evaluation and gene ranking are defined. Extensive experimental results have been provided to demonstrate the salient characteristics of the models. Most of the texts presented in this book are from our published research work. The related and relevant existing approaches or techniques are included wherever necessary. Directions for future research in the concerned topic are provided. A comprehensive bibliography on the subject is appended in each chapter, for the convenienceofreaders.Referencestosomeofthestudiesintherelatedareasmight have been omitted because of oversight or ignorance. Preface ix The book, which is unique in its character, will be useful to graduate students and researchers in computer science, electrical engineering, system science, data science, medical science, bioinformatics and information technology both as a textbookandareferencebookforsomepartsofthecurriculum.Theresearchersand practitioners in industry and R&D laboratories working in the fields of system design, pattern recognition, big data analytics, image analysis, data mining, social network analysis, computational biology, and soft computing or computational intelligence will also be benefited. Thanks to the co-authors, Dr. Avatharam Ganivada for generating various new ideasindesigninggranularnetworkmodelsandDr.ShubhraS.Rayforhisvaluable contributions to bioinformatics. It is the untiring hard work and dedication of Avatharamduringthelasttendaysthatmadeitpossibletocompletethemanuscript and submit to Springer in time. We take this opportunity to acknowledge the appreciation of Prof. Janusz KacprzykinacceptingthebooktopublishundertheSCI(StudiesinComputational Intelligence) series of Springer, and Prof. Andrzej Skowron, Warsaw University, Poland for his encouragement and support in the endeavour. We owe a vote of thankstoDr.ThomasDitzingerandDr.LavanyaDiazofSpringerforcoordinating the project, as well as the office staff of our Soft Computing Research Center for theirsupport.ThebookwaswrittenwhenProf.S.K.PalheldJ.C.BoseFellowship and Raja Ramanna Fellowship of the Govt. of India. Kolkata, India Sankar K. Pal January 2017 Principal Investigator Center for Soft Computing Research Indian Statistical Institute Contents 1 Introduction to Granular Computing, Pattern Recognition and Data Mining.... .... ..... .... .... .... .... .... ..... .... 1 1.1 Introduction ... .... ..... .... .... .... .... .... ..... .... 1 1.2 Granular Computing. ..... .... .... .... .... .... ..... .... 2 1.2.1 Granules .... ..... .... .... .... .... .... ..... .... 2 1.2.2 Granulation.. ..... .... .... .... .... .... ..... .... 3 1.2.3 Granular Relationships .. .... .... .... .... ..... .... 3 1.2.4 Computation with Granules... .... .... .... ..... .... 4 1.3 Granular Information Processing Aspects of Natural Computing .... .... ..... .... .... .... .... .... ..... .... 4 1.3.1 Fuzzy Set ... ..... .... .... .... .... .... ..... .... 4 1.3.2 Rough Set... ..... .... .... .... .... .... ..... .... 8 1.3.3 Fuzzy Rough Sets.. .... .... .... .... .... ..... .... 14 1.3.4 Artificial Neural Networks ... .... .... .... ..... .... 15 1.4 Integrated Granular Information Processing Systems . ..... .... 21 1.4.1 Fuzzy Granular Neural Network Models. .... ..... .... 21 1.4.2 Rough Granular Neural Network Models .... ..... .... 22 1.4.3 Rough Fuzzy Granular Neural Network Models.... .... 23 1.5 Pattern Recognition . ..... .... .... .... .... .... ..... .... 23 1.5.1 Data Acquisition... .... .... .... .... .... ..... .... 24 1.5.2 Feature Selection/Extraction .. .... .... .... ..... .... 25 1.5.3 Classification. ..... .... .... .... .... .... ..... .... 26 1.5.4 Clustering ... ..... .... .... .... .... .... ..... .... 27 1.6 Data Mining and Soft Computing.... .... .... .... ..... .... 29 1.7 Big Data Issues. .... ..... .... .... .... .... .... ..... .... 30 1.8 Scope of the Book .. ..... .... .... .... .... .... ..... .... 31 References.. .... .... .... ..... .... .... .... .... .... ..... .... 34 xi
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