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© 2021

Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track

European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part IV

  • Yuxiao Dong
  • Dunja Mladenić
  • Craig Saunders
Conference proceedings ECML PKDD 2020

Part of the Lecture Notes in Computer Science book series (LNCS, volume 12460)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 12460)

Table of contents

  1. Front Matter
    Pages i-xlii
  2. Applied Data Science: Recommendation

    1. Front Matter
      Pages 1-1
    2. Yuanfu Lu, Ruobing Xie, Chuan Shi, Yuan Fang, Wei Wang, Xu Zhang et al.
      Pages 3-18
    3. Taofeng Xue, Xinzhou Dong, Wei Zhuo, Beihong Jin, He Chen, Wenhai Pan et al.
      Pages 19-35
    4. Bowen Hao, Jing Zhang, Cuiping Li, Hong Chen, Hongzhi Yin
      Pages 36-51
    5. Mingxuan Yue, Tianshu Sun, Fan Wu, Lixia Wu, Yinghui Xu, Cyrus Shahabi
      Pages 52-68
    6. Junhua Liu, Kristin L. Wood, Kwan Hui Lim
      Pages 69-85
  3. Applied Data Science: Anomaly Detection

    1. Front Matter
      Pages 87-87
    2. Roghayeh Mojarad, Ferhat Attal, Abdelghani Chibani, Yacine Amirat
      Pages 89-104
    3. Wenjie Wang, Pengfei Tang, Li Xiong, Xiaoqian Jiang
      Pages 105-121
    4. Sasho Nedelkoski, Jasmin Bogatinovski, Alexander Acker, Jorge Cardoso, Odej Kao
      Pages 122-138
    5. Dilusha Weeraddana, Sudaraka MallawaArachchi, Tharindu Warnakula, Zhidong Li, Yang Wang
      Pages 139-156
    6. Seoyoung Park, Siho Han, Simon S. Woo
      Pages 157-172
  4. Applied Data Science: Web Mining

    1. Front Matter
      Pages 173-173
    2. Mingxiao An, Sundong Kim
      Pages 175-191
    3. Tianyu Cui, Gang Xiong, Gaopeng Gou, Junzheng Shi, Wei Xia
      Pages 192-207
    4. Chao Deng, Hao Wang, Qing Tan, Jian Xu, Kun Gai
      Pages 208-223
    5. Ivan Bacher, Hossein Javidnia, Soumyabrata Dev, Rahul Agrahari, Murhaf Hossari, Matthew Nicholson et al.
      Pages 224-239
    6. Rahul Tripathi, Srinivasan Jagannathan, Balaji Dhamodharaswamy
      Pages 240-256
  5. Applied Data Science: Transportation

    1. Front Matter
      Pages 257-257

Other volumes

  1. Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020): SoGood 2020, PDFL 2020, MLCS 2020, NFMCP 2020, DINA 2020, EDML 2020, XKDD 2020 and INRA 2020, Ghent, Belgium, September 14–18, 2020, Proceedings
  2. European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part I
  3. European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part II
  4. European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part III
  5. Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track
    European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part IV
  6. European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part V

About these proceedings

Introduction

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic.

The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings.

The volumes are organized in topical sections as follows:

Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion.

Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning.

Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics.

Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data.

Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.

 

 

Keywords

artificial intelligence computer networks computer security computer vision data mining databases education Human-Computer Interaction (HCI) image analysis image processing information retrieval machine learning mathematics network protocols neural networks signal processing telecommunication networks

Editors and affiliations

  • Yuxiao Dong
    • 1
  • Dunja Mladenić
    • 2
  • Craig Saunders
    • 3
  1. 1.Microsoft ResearchRedmondUSA
  2. 2.Jožef Stefan InstituteLjubljanaSlovenia
  3. 3.Amazon Alexa KnowledgeCambridgeUK

Bibliographic information