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