Discovery Science

One-Class Ensembles for Rare Genomic Sequences Identification.- Explainable and Interpretable Machine Learning.- Explaining Sentiment Classi cation with Synthetic Exemplars and Counter-Exemplars.- Generating Explainable and Effective Data Descriptors Using Relational Learning: Application to Cancer Biology.- Interpretable Machine Learning with Bitonic Generalized Additive Models and Automatic Feature Construction.- Predicting and Explaining Privacy Risk Exposure in Mobility Data.- Graph and Network Mining.- Maximizing Network Coverage Under the Presence of Time Constraint by Injecting Most Effective k-Links.- On the Utilization of Structural and Textual Information of a Scientific Knowledge Graph to Discover Future Research Collaborations: a Link Prediction Perspective.- Simultaneous Process Drift Detection and Characterization with Pattern-based Change Detectors.- Multi-Target Models.- Extreme Gradient Boosted Multi-label Trees for Dynamic ClassifierChains.- Hierarchy Decomposition Pipeline: A Toolbox for Comparison of Model Induction Algorithms on Hierarchical Multi-label Classification Problems.- Missing Value Imputation with MERCS: a Faster Alternative to MissForest.- Multi-Directional Rule Set Learning.- On Aggregation in Ensembles of Multilabel Classifiers.- Neural Networks and Deep Learning.- Attention in Recurrent Neural Networks for Energy Disaggregation.- Enhanced Food Safety Through Deep Learning for Food Recalls Prediction.- Machine learning for Modelling and Understanding in Earth Sciences.- FairNN - Conjoint Learning of Fair Representations for Fair Decisions.- Improving Deep Unsupervised Anomaly Detection by Exploiting VAE Latent Space Distribution.- Spatial, Temporal and Spatiotemporal Data.- Detecting Temporal Anomalies in Business Processes using Distance-based Methods.- Mining Constrained Regions of Interest: An Optimization Approach.- Mining Disjoint Sequential Pattern Pairs from Tourist Trajectory Data.- Predicting the Health Condition of mHealth App Users with Large Differences in the Amount of Recorded Observations - Where to Learn from.- Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method.- Time Series Regression in Professional Road Cycling.