LEADER 13897nam 22009255 450 001 9910484009303321 005 20200701100703.0 010 $a3-319-10840-9 024 7 $a10.1007/978-3-319-10840-7 035 $a(CKB)3710000000219459 035 $a(SSID)ssj0001338741 035 $a(PQKBManifestationID)11704399 035 $a(PQKBTitleCode)TC0001338741 035 $a(PQKBWorkID)11338081 035 $a(PQKB)11367494 035 $a(DE-He213)978-3-319-10840-7 035 $a(MiAaPQ)EBC6286391 035 $a(MiAaPQ)EBC5596051 035 $a(Au-PeEL)EBL5596051 035 $a(OCoLC)889319773 035 $a(PPN)18062640X 035 $a(EXLCZ)993710000000219459 100 $a20140813d2014 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aIntelligent Data Engineering and Automated Learning -- IDEAL 2014 $e15th International Conference, Salamanca, Spain, September 10-12, 2014, Proceedings /$fedited by Emilio Corchado, José A. Lozano, Héctor Quintián, Hujun Yin 205 $a1st ed. 2014. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014. 215 $a1 online resource (XX, 508 p. 147 illus.) 225 1 $aInformation Systems and Applications, incl. Internet/Web, and HCI ;$v8669 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-319-10839-5 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- Organization -- Table of Contents -- MLeNN: A First Approach to Heuristic Multilabel Undersampling -- 1 Introduction -- 2 Preliminaries -- 3 Heuristic Multilabel Undersampling with MLeNN -- 3.1 Candidate Selection -- 3.2 Labelset difference Evaluation -- 4 Experimentation and Analysis -- 4.1 Experimental Framework -- 4.2 Results and Analysis -- 5 Conclusions -- References -- Development of Eye-Blink Controlled Application for Physically Handicapped Children -- 1 Introduction -- 2 Purpose of the Study -- 3 Structure of the System -- 3.1 Detection of an Eye Area -- 3.2 Detection of Eye Opening and Closing -- 3.3 Detecting Method by Saturation in Color Space -- 3.4 Detection of Eye Opening and Closing -- 3.5 Developing Afterimage Method -- 4 Developing Communication Applications -- 4.1 Eye Talk, Eye Tell -- 5 Conclusion -- References -- Generation of Reducts Based on Nearest Neighbor Relation -- 1 Introduction -- 2 Nearest Neighbor Relation -- 3 Nearest Neighbor Relation for Reduct Generation -- 3.1 Generation of Reducts Based on Nearest Neighbor Relation with Minimal Distance -- 4 Mapping of Nearest Neighbor Relation on Modified Reducts -- 4.1 Characterization of Reducts by Using Nearest Neighbor Relation -- 5 Classification by Mapping of Nearest Neighbor Relation -- 6 Conclusion -- References -- Automatic Content Related Feedback for MOOCs Based on Course Domain Ontology -- 1 Introduction -- 2 Related Work -- 3 MOOC's Domain Ontology and Feedback -- 3.1 Phase I: Building Domain Ontology -- 3.2 Phase II: Processing Students Posts -- 3.3 Phase III: Feedback Generating -- 4 Experimental Setting and Results -- 5 Summary and Future Work -- References -- User Behavior Modeling in a Cellular Network Using Latent Dirichlet Allocation -- 1 Introduction -- 2 User Behavior Modeling. 327 $a2.1 Analogy between Topic Modeling and User Behavior Modeling -- 2.2 Overview of Latent Dirichlet Allocation -- 3 Overview of the Dataset and Implementation -- 3.1 Data Trace -- 3.2 Implementation: Mr.LDA -- 4 Discovering Hidden Interests of the Mobile Users -- 4.1 Objectives -- 4.2 Stop URLs: What Are They ? -- 4.3 Encoding of Duration: Oversampling of URLs -- 4.4 Informed Prior -- 5 Experimental Analysis -- 6 Conclusions and Future Works -- References -- Sample Size Issues in the Choice between the Best Classifier and Fusion by Trainable Combiners -- 1 Introduction -- 2 Accuracy of Best Expert Selection -- 3 Accuracy of Linear Expert Fusion -- 4 Analytical and Empirical Comparison: An Example -- 5 Concluding Remarks -- References -- On Interlinking Linked Data Sources by Using Ontology Matching Techniques and the Map-Reduce Framework -- 1 Introduction -- 2 Background and Related Work -- 2.1 Linked Data: Interlinking and Ontology Matching -- 2.2 Map-Reduce Framework -- 2.3 Related Work -- 3 Proposed Interlinking Approach -- 3.1 Ontology Matching through Datasets Linksets -- 3.2 Matching Entities by Similarity Measure -- 4 Experiments -- 5 Conclusions and Future Research Lines -- References -- Managing Borderline and Noisy Examples in Imbalanced Classification by Combining SMOTE with Ensemble Filtering -- 1 Introduction -- 2 Borderline and Noisy Examples in Imbalanced Datasets -- 2.1 Imbalanced Classification with Borderline and Noisy Examples -- 2.2 Combining SMOTE and IPF -- 3 Experimental Analysis -- 3.1 Datasets and Re-sampling Techniques for Comparison -- 3.2 Results on Synthetic Datasets -- 4 Concluding Remarks -- References -- TweetSemMiner: A Meta-Topic Identification Model for Twitter Using Semantic Analysis -- 1 Introduction -- 2 TweetSemMiner: The Twitter Meta-topic Analysis Architecture -- 2.1 TweetSemMiner Architecture. 327 $a2.2 TweetSemMiner Execution -- 3 Experimental Results -- 4 Conclusions and Future Work -- References -- Use of Empirical Mode Decomposition for Classification of MRCP Based Task Parameters -- 1 Introduction -- 2 Dataset -- 2.1 Subjects -- 2.2 Signal Acquisition Details -- 2.3 Dataset Detail -- 3 Methodology -- 3.1 Initial Feature Extraction and Results -- 3.2 Pre-processing Requirement -- 3.3 Empirical Mode Decomposition (EMD) -- 3.4 Tailored Preprocessing Method Employed -- 3.5 Enhanced Feature Extraction -- 4 Results -- 4.1 Results Using Data Cleaned through IMF Subtraction -- 4.2 Further Analysis of Dataset -- 5 Discussion -- 6 Future Work -- References -- Diversified Random Forests Using Random Subspaces -- 1 Introduction -- 2 Random Forests: An Overview -- 3 Diversified Random Forests -- 4 Experimental Study -- 5 Related Work -- 6 Conclusion and Future Work -- References -- Fast Frequent Pattern Detection Using Prime Numbers -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 The Proposed Approach -- 5 Experimental Results -- 6 Conclusion and Future Work -- References -- Multi-step Forecast Based on Modified Neural Gas Mixture Autoregressive Model -- 1 Introduction -- 2 Methodology -- 2.1 Training Vectors -- 2.2 Structure of Neurons -- 2.3 Training Procedure -- 2.4 Predicting Procedure -- 3 Experimental Results -- 3.1 Foreign Exchange Rates -- 3.2 Benchmark Data -- 4 Conclusion and Future Work -- References -- LBP and Machine Learning for Diabetic Retinopathy Detection -- 1 Introduction -- 2 Diabetic Retinopathy -- 3 The Method -- 3.1 Feature Extraction -- 3.2 Machine Learning Algorithms -- 4 Experimental Results -- 5 Conclusions -- References -- Automatic Validation of Flowmeter Data in Transport Water Networks: Application to the ATLLc Water Network -- 1 Introduction -- 2 Proposed Methodology -- 2.1 Data Validation Methodology. 327 $a2.2 Data Reconstruction Methodology -- 3 Models for Data Validation and Reconstruction -- 3.1 Spatial Model -- 3.2 Time-Series Model -- 4 Application to the ATLLc Water Network -- 5 Conclusions -- References -- Data Analysis for Detecting a Temporary Breath Inability Episode -- 1 Introduction -- 2 A Brief Description of the Methods for the Apnea Diagnosis -- 3 Decisions on Posture and Remarkable Patterns -- 3.1 Deployment of the Relevant Patterns -- 4 Experimentation and Results -- 5 Conclusions and Future Research Lines -- References -- CPSO Applied in the Optimization of a Speech Recognition System -- 1 Introduction -- 2 Methodology -- 2.1 Pre-processing Speech Signal -- 2.2 Two-Dimensional Time Matrix DCT Coding -- 2.3 Rule Base Used for Speech Recognition -- 2.4 Generation of Fuzzy Patterns -- 3 Optimization of Relational Surface with Particle Swarm -- 4 Results -- 5 Conclusion -- References -- Object-Neighbourhood Clustering Ensemble Method -- 1 Introduction -- 2 Related Work -- 3 Object-Neighbourhood Clustering Ensemble -- 4 Experimental Design -- 4.1 Experiment Procedure -- 4.2 Datasets -- 5 Results and Analysis -- 6 Conclusion -- References -- A Novel Recursive Kernel-Based Algorithm for Robust Pattern Classification -- 1 Introduction -- 2 Methods and Algorithms -- 3 Experimental Results and Discussion -- 4 Conclusions -- References -- Multi-Objective Genetic Algorithms for Sparse Least Square Support Vector Machines -- 1 Introduction -- 2 LSSVM Classifiers -- 3 Sparse Classifiers -- 3.1 Pruning LSSVM -- 3.2 IP-LSSVM -- 4 Genetic Algorithms -- 5 Proposal: Multi-Objective Genetic Algorithm for Sparse LSSVM (MOGAS-LSSVM) -- 5.1 Individuals or Chromosomes -- 5.2 Fitness Function for MOGAS-LSSVM -- 5.3 MOGAS-LSSVM Algorithm -- 6 Simulations and Discussion -- 7 Conclusions -- References. 327 $aPixel Classification and Heuristics for Facial Feature Localization -- 1 Introduction -- 2 Methodology -- 2.1 General View -- 2.2 Feature Extraction -- 2.3 Feature Selection -- 2.4 Post-processing Heuristic -- 3 Results and Discussion -- 4 Conclusion -- References -- A New Appearance Signature for Real Time Person Re-identification -- 1 Introduction -- 2 Proposed Approach -- 2.1 Feature Extraction -- 2.2 Body Stripes Selection -- 3 Experimental Results -- 3.1 Presentation of VIPeR Database and Experimental Setup -- 3.2 Evaluation of Our Appearance Signature Parameters -- 3.3 Comparison with Recent State-of-the-Art Methods -- 4 Conclusion -- References -- A New Hand Posture Recognizer Based on Hybrid Wavelet Network Including a Fuzzy Decision Support System -- 1 Introduction -- 2 Overview of the Proposed Approach to Recognize Statistic Hand Gestures -- 2.1 Approximation with HFWN -- 2.2 Classification -- 3 Experimental Results -- 4 Conclusion and Future Works -- References -- Sim-EA: An Evolutionary Algorithm Based on Problem Similarity -- 1 Introduction -- 2 Algorithm Description -- 3 Experiments and Results -- 3.1 Test Problem Definition -- 3.2 Results -- 4 Conclusion -- References -- Multiobjective Dynamic Constrained Evolutionary Algorithm for Control of a Multi-segment Articulated Manipulator -- 1 Introduction -- 2 Problem Statement -- 3 Evolutionary Algorithm -- 3.1 The Main Loop -- 4 Experiments and Results -- 5 Conclusions -- References -- Parameter Dependence in Cumulative Selection -- 1 Introduction -- 2 The Weasel Program -- 2.1 The Model -- 2.2 Results -- 3 A Genetic Algorithm Approach -- 3.1 The Model -- 3.2 Results -- 4 Conclusions -- References -- Explanatory Inference under Uncertainty -- 1 Introduction -- 2 Measures of Explanatory Power -- 3 Methodology -- 4 Simulations and Results -- 5 Conclusion -- References. 327 $aA Novel Ego-Centered Academic Community Detection Approach via Factor Graph Model. 330 $aThis book constitutes the refereed proceedings of the 15th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2014, held in Salamanca, Spain, in September 2014. The 60 revised full papers presented were carefully reviewed and selected from about 120 submissions. These papers provided a valuable collection of recent research outcomes in data engineering and automated learning, from methodologies, frameworks, and techniques to applications. In addition the conference provided a good sample of current topics from methodologies, frameworks, and techniques to applications and case studies. The techniques include computational intelligence, big data analytics, social media techniques, multi-objective optimization, regression, classification, clustering, biological data processing, text processing, and image/video analysis. 410 0$aInformation Systems and Applications, incl. Internet/Web, and HCI ;$v8669 606 $aData mining 606 $aPattern recognition 606 $aArtificial intelligence 606 $aAlgorithms 606 $aInformation storage and retrieval 606 $aComputers 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 606 $aComputation by Abstract Devices$3https://scigraph.springernature.com/ontologies/product-market-codes/I16013 615 0$aData mining. 615 0$aPattern recognition. 615 0$aArtificial intelligence. 615 0$aAlgorithms. 615 0$aInformation storage and retrieval. 615 0$aComputers. 615 14$aData Mining and Knowledge Discovery. 615 24$aPattern Recognition. 615 24$aArtificial Intelligence. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aInformation Storage and Retrieval. 615 24$aComputation by Abstract Devices. 676 $a005.74 702 $aCorchado$b Emilio$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLozano$b José A$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aQuintián$b Héctor$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aYin$b Hujun$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484009303321 996 $aIntelligent Data Engineering and Automated Learning -- IDEAL 2014$92587648 997 $aUNINA