Keywords:
Data mining-Congresses.
;
Electronic books.
Type of Medium:
Online Resource
Pages:
1 online resource (831 pages)
Edition:
1st ed.
ISBN:
9783319495866
Series Statement:
Lecture Notes in Computer Science Series ; v.10086
URL:
https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=5577322
DDC:
006.312
Language:
English
Note:
Intro -- Preface -- Organization -- Contents -- Spotlight Research Papers -- Effective Monotone Knowledge Integration in Kernel Support Vector Machines -- 1 Introduction -- 2 Background -- 2.1 Partially Monotone Classification -- 2.2 Monotone Support Vector Machines -- 3 Partially Monotone Support Vector Machines -- 3.1 PM-SVM Technique -- 3.2 Measurement of Partial Monotonicity -- 4 Experiments and Datasets -- 5 Results and Discussion -- 6 Conclusions -- References -- Textual Cues for Online Depression in Community and Personal Settings -- 1 Introduction -- 2 Method -- 2.1 Datasets -- 2.2 Feature Sets -- 2.3 Statistical Testing -- 2.4 Classification -- 3 Depression in Community and Personal Settings -- 3.1 Affective Information -- 3.2 Psycho-Linguistic Features -- 3.3 Topical Representation -- 4 Classification -- 4.1 Performance -- 4.2 Linguistic Features as the Predictors -- 4.3 Topics as the Predictors -- 5 Limitation and Further Research -- 6 Conclusion -- References -- Confidence-Weighted Bipartite Ranking -- 1 Introduction -- 2 Related Work -- 3 Online Confidence-Weighted Bipartite Ranking -- 3.1 Problem Setting -- 3.2 Update Buffer -- 3.3 Update Ranker -- 4 Experimental Results -- 4.1 Real World Datasets -- 4.2 Compared Methods and Model Selection -- 4.3 Results on Benchmark Datasets -- 4.4 Results on High-Dimensional Datasets -- 5 Conclusions and Future Work -- References -- Mining Distinguishing Customer Focus Sets for Online Shopping Decision Support -- 1 Introduction -- 2 Problem Definition -- 3 Related Work -- 3.1 Recommendation -- 3.2 Opinion Mining -- 3.3 Contrast Mining -- 4 Design of dFocus-Miner -- 4.1 Candidate Customer Focus Generation -- 4.2 Customer Focus Selection -- 4.3 Mining Top-k Distinguishing Customer Focus Sets -- 5 Empirical Evaluation -- 5.1 Case Study for Effectiveness Evaluation -- 5.2 Efficiency Evaluation.
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6 Conclusions -- References -- Community Detection in Networks with Less Significant Community Structure -- 1 Introduction -- 2 Background: LPA, LPAm and LPAm+ -- 2.1 LPA -- 2.2 LPAm -- 2.3 LPAm+ -- 3 Meta-LPAm+ -- 4 Experimental Results -- 5 Conclusions -- References -- Prediction-Based, Prioritized Market-Share Insight Extraction -- 1 Introduction -- 2 Review of Time-Series Predictors -- 3 Our Solution -- 3.1 Analytics Engine -- 3.2 Display and Interactivity -- 4 Surprise Factor -- 4.1 Logit Transform -- 5 Experiments -- 5.1 Synthetic Data -- 5.2 Real-World Data -- 6 Conclusion -- References -- Interrelationships of Service Orchestrations -- 1 Introduction -- 2 Related Work -- 3 Interrelationships of Service Orchestrations -- 3.1 Service Orchestration Discovery by Topic Modeling -- 3.2 Proposed Model for Interrelationship Discovery -- 4 Experiments -- 4.1 Service Orchestration Discovery by Topic Modeling -- 4.2 Proposed Model for Interrelationship Discovery -- 4.3 Performance Analysis -- 5 Conclusions -- References -- Outlier Detection on Mixed-Type Data: An Energy-Based Approach -- 1 Introduction -- 2 Related Work -- 3 Mixed-Type Outlier Detection -- 3.1 Density Estimation for Mixed Data -- 3.2 Mixed-Variate Restricted Boltzmann Machines -- 3.3 Outlier Detection on Mixed-Type Data -- 4 Experiments -- 4.1 Synthetic Data -- 4.2 Real Data -- 5 Discussion -- References -- Low-Rank Feature Reduction and Sample Selection for Multi-output Regression -- 1 Introduction -- 2 Preliminary -- 3 Method -- 3.1 LFR_SS Algorithm -- 3.2 Optimization -- 3.3 Proving of the Convergence -- 4 Experiments -- 4.1 Datasets and Comparison Algorithms -- 4.2 Experimental Settings -- 4.3 Regression Results -- 5 Conclusion -- References -- Biologically Inspired Pattern Recognition for E-nose Sensors -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Definition.
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3.2 E-nose Olfactory System -- 3.3 E-nose Adaptation with AORC -- 4 Experiments and Evaluation -- 4.1 E-nose Data -- 4.2 Synthetic Data Set -- 4.3 Analysis and Results -- 5 Conclusions -- References -- Addressing Class Imbalance and Cost Sensitivity in Software Defect Prediction by Combining Domain Costs and Balancing Costs -- 1 Introduction -- 1.1 Main Contributions of This Study -- 2 Related Work -- 2.1 Measuring Source Code -- 2.2 Sampling Techniques -- 2.3 Classification Methods -- 2.4 Cost-Sensitive Classification for Class Imbalance Treatment -- 3 Our Framework: BCF -- 3.1 Step 1: Generation of Class Specific Clusters (CSCs) -- 3.2 Step 2: Calculation of Record Specific Balancing Costs -- 3.3 Step 3: Combination of Domain Costs and Balancing Costs -- 3.4 Step 4: Cost-Sensitive Classification Using Modified CSForest -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Results and Discussion -- 4.3 Extracted Knowledge -- 5 Conclusion -- References -- Unsupervised Hypergraph Feature Selection with Low-Rank and Self-Representation Constraints -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Notations -- 3.2 Method -- 3.3 Optimization -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Parameter Sensitivity -- 4.3 Experimental Results -- 5 Conclusion -- References -- Improving Cytogenetic Search with GPUs Using Different String Matching Schemes -- 1 Introduction -- 2 Background -- 2.1 Related Work -- 3 Algorithms -- 3.1 Parallel Brute-Force String Matching -- 3.2 Parallel FA String Matching -- 3.3 Application to Triple Inference -- 4 Experiments -- 4.1 MESH 2016 Benchmark -- 4.2 Bio2RDF Benchmark -- 5 Conclusion and Future Work -- References -- CEIoT: A Framework for Interlinking Smart Things in the Internet of Things -- 1 Introduction -- 2 The CEIoT Approach -- 2.1 Correlation Discovery Process.
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2.2 Framework Architecture and System Entities -- 2.3 Correlation Extraction -- 2.4 Correlation Integration -- 2.5 Correlation Representation -- 3 Experimental Results -- 3.1 System Performance -- 3.2 Things Correlation Graph -- 3.3 Message Volume -- 4 Related Work -- 5 Conclusion -- References -- Adopting Hybrid Descriptors to Recognise Leaf Images for Automatic Plant Specie Identification -- 1 Introduction -- 2 Related Work -- 3 Research Problem -- 4 Descriptors -- 4.1 Global Feature Extraction -- 4.2 Local Feature Extraction -- 4.3 Hybrid Descriptor -- 5 Experimental Evaluation -- 5.1 Experimental Design -- 5.2 Experimental Result Analysis -- 6 Conclusions -- References -- Efficient Mining of Pan-Correlation Patterns from Time Course Data -- 1 Introduction -- 2 Problem Formulation -- 2.1 Correlation Patterns: Definitions -- 2.2 Unified Representation of All Correlation Patterns -- 3 Mining Algorithms -- 3.1 Transform Time-Course Data Set M into Sequential Transaction Data Set S -- 3.2 Opposite Mirror Copy of S -- 3.3 Mine Frequent Closed Sequential Value Movements in S' -- 3.4 Opposite Mirror Copy Causes Redundancy in Patterns -- 3.5 Parameter Setting -- 3.6 An Illustrative Example -- 4 Performance Evaluation and Application -- 4.1 Efficiency and Scalability Results on Synthetic Data Sets -- 4.2 Application in Time-Course Gene Expression Data -- 5 Conclusion -- References -- Recognizing Daily Living Activity Using Embedded Sensors in Smartphones: A Data-Driven Approach -- 1 Introduction -- 2 System Overview -- 2.1 Built-In Sensors -- 2.2 Defining Activity List -- 3 Methodology -- 3.1 Collection of Training Data -- 3.2 Classification Algorithms -- 4 Evaluation -- 4.1 Comparison of Different Methods -- 4.2 Optimal Selection of Parameters -- 4.3 Development of Real-Time HAR System -- 5 In-situ Experiments -- 6 Related Work.
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6.1 Wearable Sensors Based HAR -- 6.2 Environmental Sensors Based HAR -- 6.3 Smartphone Based HAR -- 7 Conclusion -- References -- Dynamic Reverse Furthest Neighbor Querying Algorithm of Moving Objects -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Uncertain Moving Object Model -- 3.2 Dynamic RFN Query Algorithm -- 4 Experiments -- 4.1 Evaluation of DRFN -- Evaluation: P 8 RFN and P 9 RFN -- 5 Conclusion and Future Work -- References -- Research Papers -- Relative Neighborhood Graphs Uncover the Dynamics of Social Media Engagement -- 1 Introduction and Background -- 2 Methodology -- 3 Results -- 4 Discussion and Conclusion -- References -- An Ensemble Approach for Better Truth Discovery -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 4 Ensemble Approaches -- 4.1 Feasibility Analysis -- 4.2 Parallel Model -- 4.3 Serial Model -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Experiments on Real-World Datasets -- 5.3 Experiments on Synthetic Datasets -- 5.4 Impact of Method Numbers on Serial Ensemble Model -- 6 Conclusion -- References -- Single Classifier Selection for Ensemble Learning -- 1 Introduction -- 2 Definitions of Accurate and Diverse Classifiers for Ensemble -- 2.1 Accurate Classifier for Ensemble -- 2.2 Diverse Classifier for Ensemble -- 3 Picking Up Single Classifiers for Ensemble -- 4 Experimental Study -- 4.1 Benchmark Data Set -- 4.2 Experimental Setup -- 4.3 Experimental Results and Analysis -- 5 Conclusions -- References -- Community Detection in Dynamic Attributed Graphs -- 1 Introduction -- 2 Related Work -- 3 Community Detection in Dynamic Attributed Graphs -- 3.1 Problem Statement -- 3.2 Algorithm for Community Detection in Dynamic Attributed Graphs -- 3.3 Benchmark Dynamic Attributed Graphs for Testing Community Detection Algorithms -- 4 Experimental Evaluation -- 4.1 Benchmark Graphs.
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4.2 Real-World Networks.
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