Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part III
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| Main Authors | , , , , , , , |
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| Format | eBook |
| Language | English |
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Springer International Publishing AG
2019
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| Edition | 1 |
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| Author | Berlingerio, Michele Brefeld, Ulf Curry, Edward Pinelli, Fabio Marascu, Alice MacNamee, Brian Hurley, Neil Daly, Elizabeth |
|---|---|
| Author_xml | – sequence: 1 fullname: Brefeld, Ulf – sequence: 2 fullname: Curry, Edward – sequence: 3 fullname: Daly, Elizabeth – sequence: 4 fullname: MacNamee, Brian – sequence: 5 fullname: Marascu, Alice – sequence: 6 fullname: Pinelli, Fabio – sequence: 7 fullname: Berlingerio, Michele – sequence: 8 fullname: Hurley, Neil |
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| SubjectTerms | Data mining-Congresses Machine learning-Congresses |
| Subtitle | European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part III |
| TableOfContents | Implicit Linking of Food Entities in Social Media -- 1 Introduction -- 2 Empirical Analysis -- 2.1 Datasets -- 2.2 Analysis -- 3 Models -- 3.1 Entity-Indicative Weighting (EW) -- 3.2 Query Expansion with Same-Venue Posts -- 3.3 Fused Model (EWQE) -- 3.4 Venue-Based Prior -- 4 Experiments -- 4.1 Setup -- 4.2 Results -- 4.3 Case Studies -- 4.4 Parameter Sensitivity -- 5 Related Work -- 6 Conclusion -- References -- A Practical Deep Online Ranking System in E-commerce Recommendation -- 1 Introduction -- 2 Related Work -- 2.1 Static Recommendation -- 2.2 Time-Aware Recommendation -- 2.3 Online Recommendation -- 3 The Deep Online Ranking System -- 3.1 Candidate Retrieval -- 3.2 Learning-to-Rank via DNN -- 3.3 Online Re-ranking via MAB -- 4 Experiments -- 4.1 Case Study -- 4.2 Experiment Setup -- 4.3 Production Performance -- 4.4 Distribution Analysis -- 4.5 System Specifications -- 5 Conclusion -- References -- ADS Engineering and Design -- Helping Your Docker Images to Spread Based on Explainable Models -- 1 Introduction -- 2 Background -- 3 Docker Images and Popularity Problems -- 4 Proposed Approach -- 4.1 Estimating Popularity -- 4.2 Recommending Improvements -- 5 Experiments -- 5.1 Dataset -- 5.2 Experimental Settings -- 5.3 Estimating Image Popularity -- 5.4 Recommending Image Improvements -- 5.5 Explaining Improvement Features -- 5.6 Portability of Our Approach -- 6 Related Work -- 7 Conclusion -- References -- ST-DenNetFus: A New Deep Learning Approach for Network Demand Prediction -- 1 Introduction -- 2 Related Work -- 3 External Factors Extraction -- 4 Deep Spatio-Temporal Dense Network with Data Fusion (ST-DenNetFus) -- 4.1 Network Throughput Input Data -- 4.2 External Factors and Fusion -- 5 Experiments Setup -- 5.1 Dataset -- 5.2 Baselines -- 6 Results -- 7 Conclusion -- References On Optimizing Operational Efficiency in Storage Systems via Deep Reinforcement Learning -- 1 Introduction -- 1.1 The Problem -- 1.2 Related Work and Our Contributions -- 2 Our DRL-Based Solution -- 2.1 Reinforcement Learning (RL) Preliminaries -- 2.2 State, Action and Reward Formulations -- 2.3 Algorithm: The Advantage Actor-Critic (A2C) Agent -- 2.4 Actor-Critic Network Architecture -- 3 Experimental Setup and Results -- 3.1 Timelines -- 3.2 I/O Scenarios -- 3.3 Hyperparameters -- 3.4 Results -- 4 Concluding Remarks -- References -- Automating Layout Synthesis with Constructive Preference Elicitation -- 1 Introduction -- 2 Related Work -- 3 Coactive Learning for Automated Layout Synthesis -- 4 Experiments -- 4.1 Furniture Arrangement -- 4.2 Floor Planning -- 5 Conclusion -- References -- Configuration of Industrial Automation Solutions Using Multi-relational Recommender Systems -- 1 Introduction -- 2 Notation and Background -- 3 Related Methods -- 3.1 General Recommender Systems -- 3.2 Knowledge Graph Methods -- 4 Our Method -- 5 Real-World Experimental Study -- 5.1 Data -- 5.2 Implementation -- 5.3 Evaluation Scheme -- 5.4 Results -- 6 Conclusion -- References -- Learning Cheap and Novel Flight Itineraries -- 1 Introduction -- 2 Data Set -- 2.1 Diversity of Airlines and Routes -- 2.2 Temporal Patterns -- 3 Predictive Construction of Combination Itineraries -- 3.1 Problem Formulation -- 3.2 Models -- 3.3 Location Representations -- 3.4 Prediction Performance -- 4 Putting the Model in Production -- 4.1 Model Parameters -- 4.2 Architecture of the Pipeline -- 4.3 Performance in Production -- 5 Related Work -- 6 Conclusions -- References -- Towards Resource-Efficient Classifiers for Always-On Monitoring -- 1 Introduction -- 2 Background and Notations -- 2.1 Bayesian Classifiers -- 2.2 Bayesian Inference and Knowledge Compilation 2 Preliminaries -- 2.1 Product Lifecycle Model -- 2.2 Reinforcement Learning and DDPG Methods -- 3 A Scalable Reinforcement Mechanism Design Framework -- 3.1 First Principal Component Based Permutation -- 3.2 Repeated Sampling Based Experiences Generation -- 4 Experimental Results -- 4.1 The Configuration -- 4.2 The Results -- 5 Conclusions and Future Work -- References -- Discovering Bayesian Market Views for Intelligent Asset Allocation -- 1 Introduction -- 2 Bayesian Asset Allocation -- 3 Methodologies -- 3.1 Modeling Market Views -- 3.2 The Confidence Matrix -- 3.3 Optimal Market Views -- 3.4 Generating Market Views with Neural Models -- 4 Experiments -- 4.1 Data -- 4.2 Trading Simulation -- 4.3 Performance Metrics -- 4.4 Findings -- 5 A Story -- 6 Conclusion and Future Work -- References -- Intent-Aware Audience Targeting for Ride-Hailing Service -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Problem Definition -- 3.2 Data Specification -- 4 The Intent Prediction Model -- 4.1 Sequential Pattern Mining -- 4.2 Map Query Based LSTM Method -- 4.3 Combination of LSTM and GBDT -- 5 Experiment -- 5.1 Data Description -- 5.2 Model Comparison -- 5.3 Experiment Results -- 6 Online Evaluation -- 7 Conclusion -- References -- A Recurrent Neural Network Survival Model: Predicting Web User Return Time -- 1 Introduction -- 2 Background -- 2.1 Survival Analysis -- 2.2 Cox Proportional Hazards Model -- 2.3 Recurrent Neural Networks, LSTM, and Embeddings -- 2.4 Recurrent Temporal Point Processes -- 3 Method -- 3.1 Heterogeneous Markers -- 3.2 Recurrent Neural Network Survival Model (RNNSM) -- 3.3 Return Time Predictions -- 4 Experiments -- 4.1 Result on Performance Metrics -- 4.2 Prediction Error in Relation to True Return Time -- 4.3 Error in Relation to Number of Active Days -- 5 Discussion -- References 4.4 No Labeled Data for Negative Samples Problem -- 5 System -- 5.1 Data Augment with Sliding Window -- 5.2 Feature Extraction Through Center Radiation -- 5.3 Ensemble Algorithm -- 6 Experiment -- 6.1 Experiment Setup -- 6.2 Experiment Result and Discussion -- 7 Conclusion -- References -- From Empirical Analysis to Public Policy: Evaluating Housing Systems for Homeless Youth -- 1 Introduction -- 1.1 Our Goal -- 2 Current Approach for Housing Prioritization -- 3 Description of the Data -- 3.1 Features from the Youth -- 3.2 Types of Exits from Homelessness -- 4 Methodology and Evaluation Metrics -- 4.1 Classifiers -- 4.2 Performance Measure -- 4.3 Building the Final Model -- 5 Decision Rules and Youths' Most Likely Exits -- 5.1 Decision Rules for Assigning Youth to PSH and RRH -- 5.2 Youths' Exits to Family and Self Resolve -- 6 Outcome of the Current Housing Assignment Process -- 7 Predicting the Outcomes of Housing Assignments -- 7.1 Logistic Regressions and Decision Trees -- 7.2 Significant Features of the Learned Models -- 8 Policy Recommendations from Our Empirical Analysis -- 9 Conclusion, Discussion, and Future Work -- References -- Discovering Groups of Signals in In-Vehicle Network Traces for Redundancy Detection and Functional Grouping -- 1 Introduction and Related Work -- 2 Preparing Traces -- 2.1 Automotive Traces -- 2.2 Data Extraction and Cleaning -- 3 Feature Engineering -- 3.1 Feature Extraction -- 3.2 Wrapper-Based Feature Selection -- 3.3 Feature Transformation -- 4 Clustering -- 4.1 Properties and Approaches -- 4.2 Suitability Analysis -- 5 Evaluation -- 5.1 Experimental Setup -- 5.2 Window Size -- 5.3 Feature Selection -- 5.4 Comparison of Clustering Algorithms -- 6 Case Study -- 7 Conclusion -- References -- ADS E-commerce -- SPEEDING Up the Metabolism in E-commerce by Reinforcement Mechanism DESIGN -- 1 Introduction 2.3 Same-Decision Probability Intro -- Foreword to Applied Data Science, Demo, and Nectar Tracks -- Preface -- Organization -- Contents -- Part III -- Contents -- Part I -- Contents -- Part II -- ADS Data Science Applications -- Neural Article Pair Modeling for Wikipedia Sub-article Matching -- 1 Introduction -- 2 Related Work -- 3 Modeling -- 3.1 Preliminaries -- 3.2 Document Encoders -- 3.3 Explicit Features -- 3.4 Training -- 4 Experiments -- 4.1 Evaluation -- 4.2 Mining Sub-articles from the Entire English Wikipedia -- 5 Conclusion and Future Work -- References -- LinNet: Probabilistic Lineup Evaluation Through Network Embedding -- 1 Introduction -- 2 Materials and Methods -- 2.1 LinNet -- 2.2 Baselines -- 2.3 Datasets -- 3 Analysis and Results -- 3.1 Prediction Accuracy -- 3.2 Probability Calibration -- 3.3 Dimensionality of LinNet -- 3.4 Season Win-Loss Record and Lineup Performance -- 4 Discussion and Conclusions -- References -- Improving Emotion Detection with Sub-clip Boosting -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 The SCB Framework -- 5 Sub-clip Classification Boosting -- 5.1 Sub-clip Generation -- 5.2 Sub-classification and Emotion Strength Pairs -- 5.3 Clip Classification ``Oracle'' -- 6 Experimental Evaluation -- 6.1 Benchmark Data Sets -- 6.2 Comparison of Alternate Methods -- 6.3 Evaluation Settings -- 6.4 Evaluating ``Oracle'' Implementations -- 6.5 Hyper Parameter Tuning -- 6.6 Overall Results -- 7 Conclusion and Future Work -- References -- Machine Learning for Targeted Assimilation of Satellite Data -- 1 Introduction -- 2 Related Work -- 3 Data -- 3.1 Global Forecast System Data -- 3.2 International Best Track Archive for Climate Stewardship (IBTrACS) -- 4 Issues and Challenges -- 4.1 Area Size Determination Problem -- 4.2 Occasional Weather Events Problem -- 4.3 Weather Events Location Problem |
| Title | Machine Learning and Knowledge Discovery in Databases |
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| Volume | 11053 |
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