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© 2021

Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track

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

  • Yuxiao Dong
  • Georgiana Ifrim
  • Dunja Mladenić
  • Craig Saunders
  • Sofie Van Hoecke
Conference proceedings ECML PKDD 2020

Part of the Lecture Notes in Computer Science book series (LNCS, volume 12461)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 12461)

Table of contents

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

    1. Front Matter
      Pages 1-1
    2. Shammi More, Simon B. Eickhoff, Julian Caspers, Kaustubh R. Patil
      Pages 3-18 Open Access
    3. Dilusha Weeraddana, Nguyen Lu Dang Khoa, Lachlan O’Neil, Weihong Wang, Chen Cai
      Pages 19-35
    4. Keeyoung Kim, JinSeok Hong, Sang-Hoon Rhee, Simon S. Woo
      Pages 36-51
    5. Jiao Sun, Mingxuan Yue, Zongyu Lin, Xiaochen Yang, Luciano Nocera, Gabriel Kahn et al.
      Pages 52-67
    6. Ricky Maulana Fajri, Samaneh Khoshrou, Robert Peharz, Mykola Pechenizkiy
      Pages 68-84
    7. Alexander Treiss, Jannis Walk, Niklas Kühl
      Pages 85-100
    8. Antoine Richard, Lior Fine, Offer Rozenstein, Josef Tanny, Matthieu Geist, Cedric Pradalier
      Pages 101-117
    9. Ferdinando Fioretto, Pascal Van Hentenryck, Terrence W. K. Mak, Cuong Tran, Federico Baldo, Michele Lombardi
      Pages 118-135
  3. Applied Data Science: Healthcare

    1. Front Matter
      Pages 137-137
    2. Youssef Dawoud, Julia Hornauer, Gustavo Carneiro, Vasileios Belagiannis
      Pages 139-154
    3. Pieter J. K. Libin, Arno Moonens, Timothy Verstraeten, Fabian Perez-Sanjines, Niel Hens, Philippe Lemey et al.
      Pages 155-170
    4. Giuseppe Cannizzaro, Michele Leone, Anna Bernasconi, Arif Canakoglu, Mark J. Carman
      Pages 187-203
    5. Derick M. Oliveira, Antônio H. Ribeiro, João A. O. Pedrosa, Gabriela M. M. Paixão, Antonio Luiz P. Ribeiro, Wagner Meira Jr.
      Pages 204-219
  4. Applied Data Science: E-Commerce and Finance

    1. Front Matter
      Pages 221-221
    2. Filipe Lauar, Cristiano Arbex Valle
      Pages 241-256
    3. Muhammad Umer Anwaar, Dmytro Rybalko, Martin Kleinsteuber
      Pages 257-272

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. European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part IV
  6. Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track
    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 bayesian networks communication systems computer networks computer systems computer vision data mining databases deep learning education Human-Computer Interaction (HCI) image processing information retrieval learning linguistics machine learning network protocols neural networks signal processing

Editors and affiliations

  • Yuxiao Dong
    • 1
  • Georgiana Ifrim
    • 2
  • Dunja Mladenić
    • 3
  • Craig Saunders
    • 4
  • Sofie Van Hoecke
    • 5
  1. 1.Microsoft ResearchRedmondUSA
  2. 2.University College DublinDublinIreland
  3. 3.Jožef Stefan InstituteLjubljanaSlovenia
  4. 4.Amazon Alexa KnowledgeCambridgeUK
  5. 5.Ghent UniversityKotrijkBelgium

Bibliographic information