Publications

Journal papers

  1. Aleksovski, D., Kocijan, J., Džeroski, S. (2015a). Model-tree ensembles for noise-tolerant system identification. Advanced Engineering Informatics 29(1):1-15
  2. Aleksovski, D., Kocijan, J., Džeroski, S. (2015b). Ensembles of linear model trees for the identification of multiple-output systems. IEEE Transactions on Fuzzy Systems (under review)
  3. Appice, A., Malerba D. (2014). Leveraging the power of local spatial autocorrelation in geophysical interpolative clustering. Data Mining and Knowledge Discovery 28(5-6):1266-1313
  4. Basile, T., Di Mauro, N., Esposito, F. (2014). Assessing document relevance by modeling citation networks with probabilistic graphs. Procedia Computer Science 38:68-75
  5. Brbic, M., Warnecke, T., Krisko, A., Supek, F. (2015). Global shifts in genome and proteome composition are very tightly coupled. Genome Biology and Evolution (under review)
  6. Carvalho, A., Gama, J. (2015). SOlDiER: Structured Output Data gEneRator, Data Mining and Knowledge Discovery (under review)
  7. Ceci, M., Loglisci, C., Macchia, L. (2014a). Ranking sentences for keyphrase extraction: A relational data mining approach. Procedia Computer Science 38:52-59
  8. Ceci, M., Pio, G., Kuzmanovski, V., Džeroski, S. (2015). Semi-supervised multi-view learning for gene network reconstruction, Bioinformatics (under review)
  9. Del Buono, N., Pio, G. (2015). Non-negative matrix tri-factorization for co-clustering: an analysis of the block matrix, Information Sciences 301:13-26
  10. Di Mauro, N., Bellodi, E., Riguzzi, F. (2015). Bandit-based Monte-Carlo structure learning of probabilistic logic programs. Machine Learning (under review)
  11. Dimitrovski, I., Kocev, D., Loskovska, S., Džeroski, S. (2014b). Fast and efficient visual codebook construction for multi-label annotation using predictive clustering trees. Pattern Recognition Letters 38:38-45
  12. Dimitrovski, I., Kocev, D., Kitanovski, I., Loskovska, S., Džeroski, S. (2015a). Improved medical image modality classification using a combination of visual and textual features. Computerized Medical Imaging and Graphics 39:14–26
  13. Dimitrovski, I., Kocev, D., Loskovska, S., Dzeroski, S. (2015b) Improving bag-of-visual words image retrieval with predictive clustering trees, Information Sciences (under review)
  14. Duarte, J., Gama, J. (2015a). Adaptive model rules from high-speed data streams, Transactions on Knowledge Discovery from Data (under review)
  15. Fanaee-T, H., Gama, J. (2014). Event labeling combining ensemble detectors and background knowledge, Progress in Artificial Intelligence 2(2-3):113-127
  16. Fanaee-T, H., Gama, J. (2015a). Eigenspace method for spatiotemporal hotspot detection, Expert Systems (in print)
  17. Fanaee-T, H., Gama, J. (2015b). EigenEvent: An algorithm for event detection from complex data streams in syndromic surveillance, Intelligent Data Analysis 19(3)(in print)
  18. Gaber, M., Gama, J., Krishnaswamy, S., Gomes, J., Stahl, F. (2014). Data stream mining in ubiquitous environments: State-of-the-art and current directions, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 4(2):116-138
  19. Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M., Bouchachia, A. (2014). A survey on concept drift adaptation, ACM Computing Surveys 46(4): art. 44
  20. Gjorgjioski, V., Kocev, D., Bončina, A., Džeroski, S., Debeljak, M. (2015). Predictive clustering of multi-dimensional time series applied to forest growing stock data for different tree sizes, Ecological Modelling (under review)
  21. Ikonomovska, E., Gama, J., Džeroski, S. (2015). Online tree-based ensembles and option trees for regression on evolving data streams, Neurocomputing 150: 458-470
  22. Jančič, S., Frisvad, J., Kocev, D., Nielsen, K., Gostinčar, C., Džeroski, S.,GundeCimerman, N. (2015). Production of secondary metabolites in extreme environments: food- and airborne Wallemia spp. produce toxic metabolites at hypersaline conditions, Journal of Biological Chemistry (under review)
  23. Jančič, S., Zalar, P., Kocev, D., Schroers, H., Džeroski, S.,Gunde-Cimerman, N. (2015). Halophily reloaded: new insights into the extremophilic life-style of Wallemia with the description of Wallemia hederae sp. nov., Fungal Diversity (under review)
  24. Kosina, P., Gama, J. (2015). Very fast decision rules for classification in data streams, Data Mining and Knowledge Discovery, vol. 29, no. 1, pp. 168-202
  25. Levatić, J., Ceci, M., Kocev, D., Džeroski, S. (2015b). Self-training for multi-target regression with tree ensembles, Machine Learning (under review)
  26. Levatić, J., Kocev, D., Džeroski, S. (2014c). The importance of the label hierarchy in hierarchical multi-label classification, Journal of Intelligent Information Systems (in press)
  27. Levatić, J., Kocev, D., Džeroski, S. (2014d). Community structure models are improved by exploiting taxonomic rank with predictive clustering trees, Ecological Modelling (in press)
  28. Loglisci, C., Ceci, M., Malerba, D. (2015). Relational mining for discovering changes in evolving networks. Neurocomputing 150: 265-288
  29. Madjarov, G., Gjorgjevikj, D., Dimitrovski, I., Džeroski, S. (2015b). The use of dataderived label hierarchies in multi-label classification, Pattern Recognition (under review)
  30. Moreira-Matias, L., Mendes-Moreira, J., de Sousa, J., Gama, J. (2015a). On improving mass transit operations by using AVL-based systems: A survey, IEEE Transactions on Intelligent Transportation Systems (under review)
  31. Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L. (2015b). Timeevolving O-D matrix estimation using high-speed GPS data streams, Expert Systems with Applications (under review)
  32. Novak Babič, M., Zalar, P., Ženko, B., Schroers, H., Džeroski, S., Gunde-Cimerman, N. (2015a). Candida and Fusarium species known as opportunistic human pathogens from customer-accessible parts of residential washing machines. Fungal Biology 119(2-3):95-113
  33. Novak Babič, M., Zalar, P., Ženko, B., Džeroski, S., Gunde-Cimerman, N. (2015b). Fungi in tap water and groundwater – an overlooked threat? Scientific Reports (under review)
  34. Oliveira, M., Guerreiro, A., Gama, J. (2014). Dynamic communities in evolving customer networks: an analysis using landmark and sliding windows, Social Network Analysis and Mining 4(1): 1-19
  35. Osojnik, A., Kocev, D., Džeroski, S. (2015a) Option trees for structured output prediction (in preparation)
  36. Osojnik, A., Panov, P., Džeroski, S. (2015b) Modeling dynamical systems with data stream mining, International Journal of Applied Mathematics and Computer Science (under review)
  37. Panov, P., Soldatova L., Džeroski S. (2014a). Ontology of core data mining entities, Data Mining and Knowledge Discovery 28(5-6):1222-2265
  38. Panov, P., Soldatova L., Džeroski S. (2015). Generic ontology of datatypes, Information Sciences (under review)
  39. Pio, G., Malerba, D., D’Elia, D., Ceci, M. (2014b). Integrating microRNA target predictions for the discovery of gene regulatory networks: a semi-supervised ensemble learning approach, BMC Bioinformatics 15 (Suppl 1), S4, 2014
  40. Pio, G., Ceci, M., D’Elia, D., Malerba, D. (2015). ComiRNet: a web-based system for the analysis of miRNA-gene regulatory networks, BMC Bioinformatics (in press)
  41. Rodrigues, P., Gama, J. (2014). Distributed clustering of ubiquitous data streams, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 4(1):38-54
  42. Sarmento, R., Cordeiro, M., Gama, J., Oliveira, M. (2015c). Large-scale social networks in telecommunications, Studies in Big Data (under review)
  43. Sebastião, R., Gama, J., Mendonça, T. (2014a). Constructing fading histograms from data streams, Progress in Artificial Intelligence 3(1):15-28
  44. Slavkov, I., Karcheska, J., Kocev, D., Džeroski, S. (2015). HMC-ReliefF: Feature ranking for hierarchical multi-label classification, Information Sciences (under review)
  45. Stojanova, D., Ceci, M., Malerba, D., Džeroski, S. (2013). Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction. BMC Bioinformatics 14:285
  46. Supek, F., Lehner, B. (2015). Differential DNA mismatch repair underlies mutation rate variation across the human genome, Nature (doi:10.1038/nature14173)
  47. Vidovic, A., Supek, F., Nikolic, A., Krisko, A. (2014). Signatures of conformational stability and oxidation resistance in proteomes of pathogenic bacteria, Cell Reports 7(5):1393-1400
  48. Vidulin, V., Šmuc, T., Supek, F. (2015). Multi-label learning to predict gene function from massive genomic datasets: An application to human gut microbiota, in preparation
  49. Zajc, J., Džeroski, S., Kocev, D., Oren, A., Sonjak, S., Tkavc, R., Gunde-Cimerman, N. (2014). Chaophilic or chaotolerant fungi: A new category of extremophiles? Frontiers in Microbiology 5(708):1-15

Books

  1. Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras. Z. (2014). New Frontiers in Mining Complex Patterns – Second International Workshop, NFMCP 2013, Held in Conjunction with ECML-PKDD 2013, Prague, Czech Republic, September 27, 2013, Revised Selected Papers. Lecture Notes in Computer Science 8399, Springer, ISBN 978-3-319-08406-0
  2. Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras. Z. (2015). New Frontiers in Mining Complex Patterns – Third International Workshop, NFMCP 2014, Held in Conjunction with ECML-PKDD 2014, Nancy, France, September 19, 2014, Revised Selected Papers. Lecture Notes in Computer Science 8983, Springer
  3. Džeroski, S., Panov, P., Kocev, D., Todorovski, L. (2014). Discovery Science – 17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014. Proceedings. Lecture Notes in Computer Science 8777, Springer 2014, ISBN 978-3-319-11811-6

Conference/Workshop papers

  1. Aleksovski, D., Kocijan, J., Džeroski, S. (2014). Model tree ensembles for the identification of multiple-output systems, Proc. 14th European Control Conference, pp. 750-755
  2. Almeida, V., Gama, J. (2014). Collaborative wind power forecast, Proc. 2014 International Conference on Adaptive and Intelligent Systems, LNCS vol. 8779, pp. 162-171
  3. Ceci, M., Lanotte, P., Fumarola, F., Cavallo, D., Malerba, D. (2014b). Completion time and next activity prediction of processes using sequential pattern mining. Proc. 17th International Conference on Discovery Science, LNCS vol. 8777, pp.49-61
  4. Dimitrovski, I., Madjarov, G., Lameski, P., Kocev, D. (2014a). Maestra at LifeCLEF 2014 Plant Task: Plant identification using visual data. Working Notes for {CLEF} 2014 Conference
  5. Duarte, J., Gama, J. (2014). Ensembles of adaptive model rules from high-speed data streams, Proc. of the 3rd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, JMLR W&CP 36, pp. 198–213
  6. Duarte, J., Gama, J. (2015b). Multi-target regression on data streams with adaptive model rules, International Joint Conferences on Artificial Intelligence (under review)
  7. Gamberger, D., Mihelčić, M., Lavrač, N. (2014) Multilayer clustering: A discovery experiment on country level trading data, Proc. of 17th International Conference on Discovery Science, LNCS vol. 8777, pp.87-98
  8. Gamberger, D., Ženko, B., Mitelpunkt, A., Lavrač, N. (2015) Multilayer clustering: Biomarker driven segmentation of Alzheimer’s disease patient population. Proc. of Int. Work-conference on Bioinformatics and Biomedical Engineering, IWBBIO 2015
  9. Ivek, I. (2014). Interpretable Low-rank Document Representations with Label-dependent Sparsity Patterns. Proc. of Workshop on Interactions between Data Mining and Natural Language Processing DMNLP, pp. 97-112
  10. Levatić, J., Ceci, M., Kocev, D., Džeroski, S. (2014a). Semi-supervised learning for multitarget regression, Proc. of the 3rd International Workshop on New Frontiers in Mining Complex Patterns held in conjunction with ECML/PKDD2014, pp. 110-123
  11. Levatić, J., Kocev, D., Džeroski, S. (2015a). The use of the label hierarchy in hierarchical multi-label classification improves performance, Proc. of the 2nd International Workshop New Frontiers in Mining Complex Patterns, selected papers, LNCS vol. 8399, pp. 162-177
  12. Levnajic, Z., Todorovski, L., Ženko, B. (2014). Inferring structure of complex dynamical systems with equation discovery, Proc. 6th International Conference on Information Technologies and Information Society ITIS 2014
  13. Madjarov, G., Delev, T., Dimitrovski, I., Gjorgjevikj, D. (2014). Evaluation of differentdata-derived label hierarchies in multi-label classification, Proc. of the 3rd International Workshop on New Frontiers in Mining Complex Patterns held in conjunction with ECML/PKDD2014, pp. 124–135
  14. Madjarov, G., Dimitrovski, I., Gjorgjevikj, D., Džeroski, S. (2015a). Evaluation of different data-derived label hierarchies in multi-label classification, Proc. of the 3nd International Workshop on New frontiers in mining complex patterns, selected papers, LNCS (to appear)
  15. Mihelčić, M., Šmuc, T., Džeroski, S., Lavrač, N. (2015). Redescription Mining with Random Forest of Predictive Clustering Trees (in preparation)
  16. Moreira-Matias, L., Mendes-Moreira, J., Gama, J., Ferreira, M. (2014a). On improving operational planning and control in public transportation networks using streaming data: A machine learning approach, Proc. of the ECML PKDD 2014: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
  17. Moreira-Matias, L., Gama, J., Mendes-Moreira, J., de Sousa, J. (2014b). An incremental probabilistic model to predict bus bunching in real-time, Proc. 13th International Symposium on Advances in Intelligent Data Analysis, LNCS vol. 8819, pp. 227-238
  18. Moreira-Matias, L., Nunes, R., Ferreira, M., Mendes-Moreira, J., Gama, J. (2014c). On predicting a call center’s workload: A discretization-based approach, Proc.21st International Symposium on Methodologies for Intelligent Systems, ISMIS 2014, LNCS vol. 8502, pp. 548-553
  19. Moreira-Matias, L., Mendes-Moreira, J., Ferreira, M., Gama, J., Damas, L. (2014d). An online learning framework for predicting the taxi stand’s profitability, Proc. 2014 IEEE 17th International Conference on Intelligent Transportation Systems, pp. 2009-2014
  20. Pereira, P., Ribeiro, R., Gama, J. (2014). Failure prediction – an application in the railway industry, Proc. of 17th International Conference on Discovery Science, LNCS vol. 8777, pp. 264-275
  21. Pio, G., Lanotte, P., Ceci, M., Malerba, D. (2014a). Mining temporal evolution of entities in a stream of textual documents. Proc. 21st International Symposium on Methodologies for Intelligent Systems, ISMIS 2014, LNCS vol. 8502, p. 50-60
  22. Pio, G., Ceci, M., D’Elia, D., Malerba, D. (2014c). Network reconstruction for the identification of miRNA: mRNA interaction networks. Proc. of ECML/PKDD 2014,
    LNCS vol. 8726, pp. 508-511
  23. Pravilovic, S., Appice, A., Malerba, D. (2014a). Integrating cluster analysis to the ARIMA model for forecasting geosensor data, Proc. 21st International Symposium on Methodologies for Intelligent Systems, ISMIS 2014, LNCS 8502, pp. 234-243
  24. Pravilovic, S., Appice, A., Lanza, A., Malerba, D. (2014b). Wind power forecasting using time series cluster analysis, Proc. of 17th International Conference on Discovery Science, LNCS vol. 8777, pp. 276-287
  25. Sarmento, R., Cordeiro, M., Gama, J. (2014). Visualization for streaming networks, Proc. 3rd Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2014), pp. 62-74
  26. Sarmento, R., Cordeiro, M., Gama, J. (2015a). Visualization of evolving large scale egonetworks, Proc. of the 30th ACM/SIGAPP Symposium On Applied Computing
  27. Sarmento, R., Cordeiro, M., Gama, J., Oliveira, M. (2015b). Sampling streaming networks using top-K method, Proc. International Conference on Enterprise Information Systems
  28. Sebastião, R., Gama, J., Mendonça, T. (2014b). Comparing data distribution using fading histograms, Proc. ECAI 2014 – 21st European Conference on Artificial Intelligence (including Prestigious Applications of Intelligent Systems – PAIS 2014), pp. 1095-1096
  29. Slavkov, I., Karcheska, J., Kocev, D., Kalajdziski, S., Džeroski, S. (2014). ReliefF for hierarchical multi-label classification, Proc. of the 2nd International Workshop New Frontiers in Mining Complex Patterns, selected papers, LNCS vol. 8399, pp. 148-161
  30. Stojanovski, D., Dimitrovski, I., Madjarov, G. (2014). TweetViz: Twitter data visualization. Conference on Data Mining and Data Warehouses (SiKDD 2014), pp. 34-37
  31. Stojanovski, D., Strezoski, G., Madjarov, G., Dimitrovski, I. (2015). Deep convolutional neural networks for Twitter sentiment analysis, Proc. 10th International Conference on Hybrid Artificial Intelligence Systems HAIS 2015 (under review)
  32. Strezoski, G., Stojanovski, D., Dimitrovski, I., Madjarov, G. (2015). Content based image retrieval for large biomedical image archives, Proc. 10th International Conference on Hybrid Artificial Intelligence Systems HAIS 2015 (under review)
  33. Trojacanec, K., Kitanovski, I., Dimitrovski, I., Loshkovska, S. (2015). Content based retrieval of MRI based on brain structure changes in Alzheimer’s disease. Proc. of the 2nd International Conference on Bioimaging
  34. Vinagre, J., Jorge, A., Gama, J. (2015). Collaborative filtering with recency-based negative, Proc. ACM/SIGAPP Symposium on Applied Computing, pp. 963-965
  35. Vu, A., Morales, G., Gama, J., Bifet, A. (2014). Distributed adaptive model rules for mining big data streams, Proc. of BigData ’14: 2014 IEEE International Conference on Big Data

PhD Theses

  1. Aleksovski, D. (2014). Tree Ensembles for Discrete-time Modeling of Non-linear Dynamic Systems, PhD Thesis, Jožef Stefan International Postgraduate School, Ljubljana, Slovenia
  2. Cordeiro, M. (2015). Real Time Social Media Analysis, PhD Thesis, University of Porto (in preparation)
  3. Gjorgjioski, V. (2015). Distance-based Learning from Structured Data, PhD Thesis, Jožef Stefan International Postgraduate School, Ljubljana, Slovenia
  4. Fanaee-T, H. (2015). Event Detection from Complex Evolving Data, PhD thesis, Faculty of Engineering, University of Porto.
  5. Pio, G. (2015). Sub-network Identification and Link Prediction in Heterogeneous Networks. Ph.D. Thesis. University of Bari, Italy, 2015.
  6. Silva, J. (2015). Online Recommender Systems, PhD Thesis, University of Porto (in preparation)
  7. Moreira-Matias, L. (2015). On Improving Operational Planning and Control in Public Transportation Networks using Streaming Data: a Machine Learning Approach, PhD Thesis, University of Porto

MSc theses

  1. Casais, C. (2014). Modelo de Influência de Captação de Clientes em Comunicações Móveis: Uma Abordagem Baseada em Ego Redes, MSc Thesis, University of Porto
  2. Cerqueira, V. (2014). Dinâmicas de Comunidades em Redes Sociais de Grandes Dimensões, MSc Thesis, University of Porto
  3. Meireles, D. (2014). Previsão de Churn em Telecomunicações, MSc Thesis, University of Porto.
  4. Pereira, P. (2014). Failure Prediciton – An Application in the Railway Industry, MSc Thesis, University of Porto.
  5. Oliveira, L. (2014). Modelos de Agregação de Previsões Aplicados à Previsão de Energia Eólica, MSc Thesis, University of Porto.

Technical reports

  1. Ivek, I. (2015). Probabilistic nonnegative dictionary learning with group sparsity constraints, Technical Report, RBI
  2. Mihelčič, M. (2014). Predicting cluster of orthologous groups’ function from bacterial genome data, Seminar work, Jožef Stefan International postgraduate school, Ljubljana.
  3. Vidulin, V. (2014). Three supervised machine learning approaches for microbial gene function prediction, Technical Report, RBI

Posters

  1. Panov, P., Soldatova L., Džeroski S. (2014b). Ontology of core data mining entities, Oral presentation and poster at the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML/PKDD), Nancy, France
  2. Panov, P., Soldatova L., Džeroski S. (2014c). Ontology of data mining, Poster presentation at the 4th Plenary Meeting of Research Data Aliance (RDA), Amsterdam, The Netherlands
  3. Panov, P., Soldatova L., Džeroski S. (2014d). Ontology of data mining, Poster presentation at the 1st European Ontology Network (EUON) Workshop, Amsterdam, The Netherlands
  4. Vidulin, V., Šmuc, T., Supek, F. (2014). Speed and accuracy benchmarks of large-scale microbial gene function prediction with supervised machine learning, Late Breaking Paper @ 17th International Conference on Discovery Science