Accepted Papers

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 Saso Dzeroski
  • 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 Algorithm 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
  • Decomposition of hierarchical multi-label classification problems
    Vedrana Vidulin and Saso Dzeroski
  • 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, Han Zang, Michael 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 DM datasets: An ontology-based annotation schema
    Ana Kostovska, Sašo Džeroski and Panče Panov
  • Semi-supervised segmentation of 3D meshes via Siamese networks
    Nikita Torgashov, Roman Bezborodov and Andrey Filchenkov
  • 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