Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I

Research output: Books and anthologiesConference proceedingsResearch


  • Ulf Brefeld (Editor)
  • Elisa Fromont (Editor)
  • Andreas Hotho (Editor)
  • Arno Knobbe (Editor)
  • Marloes Maathuis (Editor)
  • Céline Robardet (Editor)

The proceedings contain 42 papers. The special focus in this conference is on Machine Learning and Knowledge Discovery in Databases. The topics include: Advocating for Multiple Defense Strategies Against Adversarial Examples; hybrid Connection and Host Clustering for Community Detection in Spatial-Temporal Network Data; collaborative Learning Based Effective Malware Detection System; a Hybrid Recommendation System Based on Bidirectional Encoder Representations; leveraging Multi-target Regression for Predicting the Next Parallel Activities in Event Logs; a Multi-view Ensemble of Deep Models for the Detection of Deviant Process Instances; exploiting Temporal Convolution for Activity Prediction in Process Analytics; hyper-Parameter Optimization for Privacy-Preserving Record Linkage; group-Specific Training Data; reasoning About Neural Network Activations: An Application in Spatial Animal Behaviour from Camera Trap Classifications; scalable Blocking for Very Large Databases; address Validation in Transportation and Logistics: A Machine Learning Based Entity Matching Approach; linking Heterogeneous Data for Food Security Prediction; towards Better Evaluation of Multi-target Regression Models; assessing the Difficulty of Labelling an Instance in Crowdworking; experimental Evaluation of Scale, and Patterns of Systematic Inconsistencies in Google Trends Data; assessing the Uncertainty of the Text Generating Process Using Topic Models; a Ranking Stability Measure for Quantifying the Robustness of Anomaly Detection Methods; interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges; Efficient Estimation of General Additive Neural Networks: A Case Study for CTG Data; practical Lessons from Generating Synthetic Healthcare Data with Bayesian Networks; what Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversations; interpretable Privacy with Optimizable Utility.

Original languageEnglish
Place of PublicationCham
PublisherSpringer Nature Switzerland AG
Number of pages766
ISBN (Print)978-3-030-46149-2
ISBN (Electronic)978-3-030-46150-8
Publication statusPublished - 2020
EventJoint European Conference on Machine Learning and Knowledge Discovery in Databases - ECML PKDD 2019 - Würzburg, Germany
Duration: 16.09.201920.09.2019

Publication series

NameLecture notes in computer science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameLecture Notes in Artificial Intelligence
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349