KANDINSKY Patterns as IQ-Test for machine learning -- Machine Learning Explainability Through Comprehensible Decision Trees -- New Frontiers in Explainable AI: Understanding the GI to Interpret the GO -- Automated Machine Learning for Studying the Trade-off Between Predictive Accuracy and Interpretability -- Estimating the Driver Status Using Long Short Term Memory -- Using Relational Concept Networks for Explainable Decision Support -- Physiological Indicators for User Trust in Machine Learning with Influence Enhanced Fact-Checking -- Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Deep Learning and Image Augmentation -- Semi-automated Quality Assurance for Domain-expert-driven Data Exploration - An Application to Principal Component Analysis -- Ranked MSD: A New Feature Ranking and Feature Selection Approach for Biomarker Identification -- How to improve the adaptation phase of the CBR in the medical domain -- Machine Learning for Family Doctors: A Case of Cluster Analysis for studying Aging Associated Comorbidities and Frailty -- Knowledge Extraction for Cryptographic Algorithm Validation Test Vectors by Means of Combinatorial Coverage Measurement -- An Evaluation on Robustness and Utility of Fingerprinting Schemes -- Differentially Private Obfuscation of Facial Images -- Insights into Learning Competence through Probabilistic Graphical Models -- Sparse Nerves in Practice -- Backdoor Attacks in Neural Networks - a Systematic Evaluation on Multiple Traffic Sign Datasets -- Deep Learning for Proteomics Data for Feature Selection and Classification -- Package and Classify Wireless Product Features to Their Sales Items and Categories Automatically -- Temporal diagnosis of discrete-event systems with dual knowledge Compilation -- A Case for Guided Machine Learning -- Using Ontologies to Express Prior Knowledge for Genetic Programming -- Real Time Hand Movement Trajectory Tracking for Enhancing Dementia Screening in Ageing Deaf Signers of British Sign Language -- Commonsense Reasoning using Theorem Proving and Machine Learning -- Deep structured semantic model for recommendations with heterogeneous side information in e-commerce.

This book constitutes the refereed proceedings of the IFIP TC 5, TC 12, WG 8.4, 8.9, 12.9 International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2019, held in Canterbury, UK, in August 2019. The 25 revised full papers presented were carefully reviewed and selected from 45 submissions. The cross-domain integration and appraisal of different fields provides an atmosphere to foster different perspectives and opinions; it will offer a platform for novel ideas and a fresh look on the methodologies to put these ideas into business for the benefit of humanity.