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Machine Learning and Knowledge Discovery in Databases

International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Peggy Cellier

    1. Institut National des Sciences Appliquées, Rennes, France

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    You can also search for this editor in PubMed   Google Scholar

  2. Kurt Driessens

    1. Maastricht University, Maastricht, The Netherlands

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Communications in Computer and Information Science (CCIS, volume 1167)

Included in the following conference series:

Conference proceedings info: ECML PKDD 2019.

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Table of contents (52 papers)

  1. Front Matter

    Pages i-xx
    Download chapter PDF

  2. Automating Data Science

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. The ABC of Data: A Classifying Framework for Data Readiness

      • Laurens A. Castelijns, Yuri Maas, Joaquin Vanschoren
      Pages 3-16

    3. Automating Common Data Science Matrix Transformations

      • Lidia Contreras-Ochando, Cèsar Ferri, José Hernández-Orallo
      Pages 17-27

    4. DeepNotebooks: Deep Probabilistic Models Construct Python Notebooks for Reporting Datasets

      • Claas Völcker, Alejandro Molina, Johannes Neumann, Dirk Westermann, Kristian Kersting
      Pages 28-43

    5. HyperUCB: Hyperparameter Optimization Using Contextual Bandits

      • Maryam Tavakol, Sebastian Mair, Katharina Morik
      Pages 44-50

    6. Learning Parsers for Technical Drawings

      • Dries Van Daele, Nicholas Decleyre, Herman Dubois, Wannes Meert
      Pages 51-56

    7. Meta-learning of Textual Representations

      • Jorge G. Madrid, Hugo Jair Escalante, Eduardo Morales
      Pages 57-67

    8. ReinBo: Machine Learning Pipeline Conditional Hierarchy Search and Configuration with Bayesian Optimization Embedded Reinforcement Learning

      • Xudong Sun, Jiali Lin, Bernd Bischl
      Pages 68-84

    9. Supervised Human-Guided Data Exploration

      • Emilia Oikarinen, Kai Puolamäki, Samaneh Khoshrou, Mykola Pechenizkiy
      Pages 85-101

    10. SynthLog: A Language for Synthesising Inductive Data Models (Extended Abstract)

      • Yann Dauxais, Clément Gautrais, Anton Dries, Arcchit Jain, Samuel Kolb, Mohit Kumar et al.
      Pages 102-110

    11. The autofeat Python Library for Automated Feature Engineering and Selection

      • Franziska Horn, Robert Pack, Michael Rieger
      Pages 111-120

    12. The Extended Dawid-Skene Model

      • Michael P. J. Camilleri, Christopher K. I. Williams
      Pages 121-136

    13. Towards Automated Configuration of Stream Clustering Algorithms

      • Matthias Carnein, Heike Trautmann, Albert Bifet, Bernhard Pfahringer
      Pages 137-143

  3. Advances in Interpretable Machine Learning and Artificial Intelligence & eXplainable Knowledge Discovery in Data Mining (AIMLAI-XKDD)

    1. Front Matter

      Pages 145-145
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    2. Effect of Superpixel Aggregation on Explanations in LIME – A Case Study with Biological Data

      • Ludwig Schallner, Johannes Rabold, Oliver Scholz, Ute Schmid
      Pages 147-158

    3. Global Explanations with Local Scoring

      • Mattia Setzu, Riccardo Guidotti, Anna Monreale, Franco Turini
      Pages 159-171

    4. Adversarial Robustness Curves

      • Christina Göpfert, Jan Philip Göpfert, Barbara Hammer
      Pages 172-179

    5. Enriching Visual with Verbal Explanations for Relational Concepts – Combining LIME with Aleph

      • Johannes Rabold, Hannah Deininger, Michael Siebers, Ute Schmid
      Pages 180-192

    6. Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability

      • Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl
      Pages 193-204
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Other volumes

  1. Machine Learning and Knowledge Discovery in Databases

  2. Machine Learning and Knowledge Discovery in Databases

  3. Machine Learning and Knowledge Discovery in Databases

  4. Machine Learning and Knowledge Discovery in Databases

  5. Machine Learning and Knowledge Discovery in Databases

Keywords

About this book

This two-volume set constitutes the refereed proceedings of the workshops which complemented the 19th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in Würzburg, Germany, in September 2019. 

The 70 full papers and 46 short papers presented in the two-volume set were carefully reviewed and selected from 200 submissions. The two volumes (CCIS 1167 and CCIS 1168) present the papers that have been accepted for the following workshops: Workshop on Automating Data Science, ADS 2019; Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence and eXplainable Knowledge Discovery in Data Mining, AIMLAI-XKDD 2019;  Workshop on Decentralized Machine Learning at the Edge, DMLE 2019; Workshop on Advances in Managing and Mining Large Evolving Graphs, LEG 2019; Workshop on Data and Machine Learning Advances with Multiple Views; Workshop on New Trends in Representation Learning with KnowledgeGraphs; Workshop on Data Science for Social Good, SoGood 2019; Workshop on Knowledge Discovery and User Modelling for Smart Cities, UMCIT 2019; Workshop on Data Integration and Applications Workshop, DINA 2019; Workshop on Machine Learning for Cybersecurity, MLCS 2019; Workshop on Sports Analytics: Machine Learning and Data Mining for Sports Analytics, MLSA 2019; Workshop on Categorising Different Types of Online Harassment Languages in Social Media; Workshop on IoT Stream for Data Driven Predictive Maintenance, IoTStream 2019; Workshop on Machine Learning and Music, MML 2019; Workshop on Large-Scale Biomedical Semantic Indexing and Question Answering, BioASQ 2019. 


The chapter "Supervised Human-guided Data Exploration" is published open access under a Creative Commons Attribution 4.0 International license (CC BY).



Editors and Affiliations

  • Institut National des Sciences Appliquées, Rennes, France

    Peggy Cellier

  • Maastricht University, Maastricht, The Netherlands

    Kurt Driessens

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