Regular Papers
- Attention in Recurrent Neural Networks for Energy Disaggregation
Nikolaos Virtsionis Gkalinikis, Christoforos Nalmpantis and Dimitris Vrakas - Constrained Clustering via Post-Processing
Nguyen Viet Dung Nghiem, Christel Vrain, Thi-Bich-Hanh Dao and Ian Davidson - COVID-19 therapy target discovery with context-aware literature mining
Matej Martinc, Blaž Škrlj, Sergej Pirkmajer, Nada Lavrač, Bojan Cestnik, Martin Marzidovšek and Senja Pollak - Evaluating Decision Makers over Selectively Labelled Data: A Causal Modelling Approach
Riku Laine, Antti Hyttinen and Michael Mathioudakis - Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars
Orestis Lampridis, Riccardo Guidotti and Salvatore Ruggieri - Extreme Algorithm Selection with Dyadic Feature Representation
Alexander Tornede, Marcel Wever and Eyke Hüllermeier - Extreme Gradient Boosted Multi-label Trees for Dynamic Classifier Chains
Simon Bohlender, Eneldo Loza Mencía and Moritz Kulessa - FABBOO – Online Fairness-aware Learning under Class Imbalance
Vasileios Iosifidis and Eirini Ntoutsi - FEAT: A Fairness-enhancing and Concept-adapting Decision Tree Classifier
Wenbin Zhang and Albert Bifet - Generating Explainable and Effective Data Descriptors Using Relational Learning: Application to Cancer Biology
Oghenejokpeme Orhobor, Joseph French, Larisa Soldatova and Ross King - Improving deep unsupervised anomaly detection by exploiting VAE latent space distribution
Fabrizio Angiulli, Fabio Fassetti and Luca Ferragina - Interpretable Machine Learning with Bitonic Generalized Additive Models and Automatic Feature Construction
Noëlie Cherrier, Michael Mayo, Jean-Philippe Poli, Maxime Defurne and Franck Sabatié - Iterative Multi-Mode Discretization: Applications to Co-Clustering
Hadi Fanaee-T and Magne Thoresen - Learning surrogates of a radiative transfer model for the Sentinel 5P satellite
Jure Brence, Jovan Tanevski, Jennifer Adams, Edward Malina and Sašo Džeroski - Maximizing Network Coverage Under the Presence of Time Constraint by Injecting Most Effective k-Links
Kouzou Ohara, Takayasu Fushimi, Kazumi Saito, Masahiro Kimura and Hiroshi Motoda - Mining Constrained Regions of Interest: An Optimization Approach
Alexandre Dubray, Guillaume Derval, Pierre Schaus and Siegfried Nijssen - Missing value imputation with MERCS: a faster alternative to MissForest when retraining is infeasible
Elia Van Wolputte and Hendrik Blockeel - Mitigating Discrimination in Clinical Machine Learning Decision Support using Algorithmic Processing Techniques
Emma Briggs and Jaakko Hollmén - Multi-Directional Rule Set Learning
Jonas Schouterden, Jesse Davis and Hendrik Blockeel - Nets versus trees for feature ranking and gene network inference
Nicolas Vecoven, Jean-Michel Begon, Antonio Sutera, Pierre Geurts and Vân Anh Huynh-Thu - On Aggregation in Ensembles of Multilabel Classifiers
Vu-Linh Nguyen, Eyke Hüllermeier, Michael Rapp, Eneldo Loza Mencía and Johannes Fürnkranz - One-Class Ensembles for Rare Genomic Sequences Identification
Jonathan Kaufmann, Kathryn Asalone, Roberto Corizzo, Colin Saldanha, John Bracht and Nathalie Japkowicz - Pathway Activity Score Learning for Dimensionality Reduction of Gene Expression Data
Ioulia Karagiannaki, Yannis Pantazis, Ekaterini Chatzaki and Ioannis Tsamardinos - Simultaneous Process Drift Detection and Characterization with Pattern-based Change Detectors
Angelo Impedovo, Paolo Mignone, Corrado Loglisci and Michelangelo Ceci - Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method
Leo Tišljarić, Sofia Fernandes, Tonči Carić and João Gama - Unsupervised Concept Drift Detection using a Student–Teacher Approach
Vitor Cerqueira, Heitor Gomes and Albert Bifet
Short Papers
- Assembled Feature Selection For Credit Scoring in Microfinance With Non-Traditional Features
Saulo Carpio, Pedro Gomes, Luis Rodrigues and João Gama - Balancing between scalability and accuracy in time-series classification for stream and batch settings
Apostolos Glenis and George Vouros - Hierarchy decomposition pipeline: A toolbox for comparison of model induction algorithms on hierarchical multi-label classification problems
Vedrana Vidulin and Sašo Džeroski - DeCStor: A Framework for Privately and Securely Sharing Files Using a Public Blockchain
Maria Siopi, George Vlahavas, Kostas Karassavas and Athena Vakali - Deep Convolutional Embedding for Painting Clustering: Case Study on Picasso’s Artworks
Giovanna Castellano and Gennaro Vessio - Detecting Temporal Anomalies in Business Processes using Distance-based Methods
Ioannis Mavroudopoulos and Anastasios Gounaris - Dynamic Incremental Semi-Supervised Fuzzy Clustering for Bipolar Disorder Episode Prediction
Gabriella Casalino, Giovanna Castellano, Francesco Galetta and Katarzyna Kaczmarek-Majer - Enhanced food safety through Deep Learning for food recalls prediction
Georgios Makridis, Philip Mavrepis, Dimosthenis Kyriazis, Ioanna Polychronou and Stathis Kaloudis - FairNN – Conjoint Learning of Fair Representations for Fair Decisions
Tongxin Hu, Vasileios Iosifidis, Wentong Liao, Hang Zhang, Michael Ying Yang, Eirini Ntoutsi and Bodo Rosenhahn - Federated Ensemble Regression using Classification
Oghenejokpeme Orhobor, Larisa Soldatova and Ross King - Investigating parallelization of MAML
Jan Bollenbacher, Florian Soulier, Beate Rhein and Laurenz Wiskott - Mining Disjoint Sequential Pattern Pairs from Tourist Trajectory Data
Siqi Peng and Akihiro Yamamoto - On the utilization of structural and textual information of a scientific knowledge graph to discover future research collaborations: a link prediction perspective
Nikolaos Giarelis, Nikos Kanakaris and Nikos Karacapilidis - Predicting and Explaining Privacy Risk Exposure in Mobility Data
Francesca Naretto, Roberto Pellungrini, Anna Monreale, Franco Maria Nardini and Mirco Musolesi - Predicting the Health Condition of mHealth App Users with Large Differences in the Amount of Recorded Observations – Where to Learn from?
Vishnu Unnikrishnan, Yash Shah, Miro Schleicher, Mirela Strandzheva, Plamen Dimitrov, Doroteya Velikova, Ruediger Pryss, Johannes Schobel, Winfried Schlee and Myra Spiliopoulou - Semantic annotation of predictive modelling experiments
Ilin Tolovski, Sašo Džeroski and Panče Panov - Semantic description of data mining datasets: An ontology-based annotation schema
Ana Kostovska, Sašo Džeroski and Panče Panov - Time Series Regression in Professional Road Cycling
Arie-Willem de Leeuw, Mathieu Heijboer, Mathijs Hofmijster, Stephan van der Zwaard and Arno Knobbe - WeakAL: Combining Active Learning and Weak Supervision
Julius Gonsior, Maik Thiele and Wolfgang Lehner