Publications

To download the list of INFER publications as a single BibTex file click here.

 S  Soft Sensors and the Process Industry   A  Adavanced Adaptation Mechanisms 
 M  Meta-learning and Methodologies of Predictive Modelling   C  Complexity Science and Complex Systems 
    THEME
 

2014

S A M C
1. Al-Jubouri, B., and Gabrys, B., 2014. Multicriteria Approaches for Predictive Model Generation: A Comparative Experimental Study. Submitted to the IEEE SSCI 2014, July 2014.     X X
2. Žliobaitė, I., Budka, M. and Stahl, F., 2014. Towards cost-sensitive adaptation: When is it worth updating your predictive model? Neurocomputing, (In press).   X X X
3. Ma, J., 2014. Chapter 16 - Using Event Reasoning for Trajectory Tracking. In: Akhgar, B. and Arabnia, H. R., eds. Emerging Trends in ICT Security. Morgan Kaufmann, pp. 253-266. DOI: 10.1016/B978-0-12-411474-6.00016-5.       X
4. Krol, D., Budka, M. and Musial, K., 2014. Simulating the information diffusion process in complex networks using push and pull strategies. ENIC 2014: European Network Intelligence Conference, September 2014.       X
5. Bouchachia, A., 2014. Online data processing. Neurocomputing, 126 (0), pp. 116-117. DOI: 10.1016/j.neucom.2013.05.008.   X X X
6. Balaguer-Ballester, E., Tabas-Diaz, A. and Budka, M., 2014. Empirical Identification of Non-stationary Dynamics in Time Series of Recordings. In: Bouchachia, A., ed. Adaptive and Intelligent Systems (8779). Springer International Publishing, pp. 142-151. DOI: 10.1007/978-3-319-11298-5_15.   X    
7. Balaguer-Ballester, E., Tabas-Diaz, A. and Budka, M., 2014. Can We Identify Non-Stationary Dynamics of Trial-to-Trial Variability? PLoS ONE, 9 (4), pp. e95648. DOI: 10.1371/journal.pone.0095648.   X    
8. Bouchachia, A. and Vanaret, C., 2014. GT2FC: An Online Growing Interval Type-2 Self-Learning Fuzzy Classifier. Fuzzy Systems, IEEE Transactions on, 22 (4), pp. 999-1018. DOI: 10.1109/TFUZZ.2013.2279554.   X X  
9. Budka, M., 2014. Data stream synchronization for defining meaningful fMRI classification problems. Applied Soft Computing , 24 (0), pp. 212-221. DOI: 10.1016/j.asoc.2014.07.011.   X    
10. Budka, M., Eastwood, M., Gabrys, B., Kadlec, P., Salvador, M. M., Schwan, S., Tsakonas, A. and Zliobaite, I., 2014. From Sensor Readings to Predictions: on the Process of Developing Practical Soft Sensors. 13th International Symposium on Intelligent Data Analysis (IDA'2014), Lueven, Belgium, Oct. 2014. X X    
11. Gama, J. a., Žliobaite, I., Bifet, A., Pechenizkiy, M. and Bouchachia, A., 2014. A Survey on Concept Drift Adaptation. ACM Comput. Surv., 46 (4), pp. 44:1-44:37. DOI: 10.1145/2523813.   X    
12. Nowak, P., Czeczot, J., Klopot, T., Szymura, M. and Gabrys, B., 2014. Linearizing Controller for Higher-Degree Nonlinear Processes with Compensation for Modeling Inaccuracies: Practical Validation and Future Developments. ICINCO'2014 conference. Vienna, Austria, Aug. 2014. X     X
13. Salvador, M., Gabrys, B. and Zliobaite, I. , 2014. Online detection of shutdown periods in chemical plants: a case study. 18th International Conference on Knowledge-based Intelligent Engineering Systems (KES'2014), Gdynia, Poland, September 2014. X X    
14. Tsakonas, A., 2014. An analysis of accuracy-diversity trade-off for hybrid combined system with multiobjective predictor selection. Applied Intelligence, 40 (4), pp. 710-723. DOI: 10.1007/s10489-013-0507-8.       X
15. Žliobaitė, I., 2014. Controlled permutations for testing adaptive learning models. Knowledge and Information Systems, 39 (3), pp. 565-578. DOI: 10.1007/s10115-013-0629-7.   X    
16. Žliobaitė, I. and Gabrys, B., 2014. Adaptive Preprocessing for Streaming Data. Knowledge and Data Engineering, IEEE Transactions on, 26 (2), pp. 309-321. DOI: 10.1109/TKDE.2012.147. X X    
17. Žliobaitė, I., Bifet, A., Pfahringer, B. and Holmes, G., 2014. Active Learning With Drifting Streaming Data. Neural Networks and Learning Systems, IEEE Transactions on, 25 (1), pp. 27-39. DOI: 10.1109/TNNLS.2012.2236570.   X X X
18. Fay, D., Moore, A. W., Brown, K., Filosi, M. and Jurman, G., 2014. Graph metrics as summary statistics for Approximate Bayesian Computation with application to network model parameter estimation. Journal of Complex Networks, , pp. cnu009. DOI: 10.1093/comnet/cnu009.       X
 

2013

S A M C
1. Ang, H. H., Gopalkrishnan, V., Žliobaitė, I., Pechenizkiy, M. and Hoi, S., 2013. Predictive Handling of Asynchronous Concept Drifts in Distributed Environments. Knowledge and Data Engineering, IEEE Transactions on, 25 (10), pp. 2343-2355. DOI: 10.1109/TKDE.2012.172.   X   X
2. Musial, K., Budka, M. and Blysz, W., 2013. Understanding the Other Side - The Inside Story of the INFER Project. In: Howlett, R. J., Gabrys, B., Musial-Gabrys, K. and Roach, J., eds. Innovation through Knowledge Transfer 2012 (18). Springer Berlin Heidelberg, pp. 1-9. DOI: 10.1007/978-3-642-34219-6_1. X X X  
3. Stahl, F. and Bramer, M., 2013. Scaling up classification rule induction through parallel processing. The Knowledge Engineering Review, 28 (04), pp. 451-478. DOI: 10.1017/S0269888912000355.       X
4. Bakirov, R. and Gabrys, B., 2013. Investigation of Expert Addition Criteria for Dynamically Changing Online Ensemble Classifiers with Multiple Adaptive Mechanisms. In: Papadopoulos, H., Andreou, A., Iliadis, L. and Maglogiannis, I., eds. Artificial Intelligence Applications and Innovations (412). Springer Berlin Heidelberg, pp. 646-656. DOI: 10.1007/978-3-642-41142-7_65.   X    
5. Balaguer-Ballester, E., 2013. Nonlinear Time Series Analyses in Industrial Environments and Limitations for Highly Sparse Data. In: Howlett, R. J., Gabrys, B., Musial-Gabrys, K. and Roach, J., eds. Innovation through Knowledge Transfer 2012 (18). Springer Berlin Heidelberg, pp. 51-60. DOI: 10.1007/978-3-642-34219-6_6.   X    
6. Bouchachia, A., Lughofer, E. and Sanchez, D., 2013. Editorial of the special issue: Online fuzzy machine learning and data mining. Information Sciences , 220 (0), pp. 1-4. DOI: 10.1016/j.ins.2012.10.005.   X X X
7. Budka, M. and Gabrys, B., 2013. Density Preserving Sampling: Robust and Efficient Alternative to Cross-validation for Error Estimation. IEEE Transactions on Neural Networks and Learning Systems, 24 (1), pp. 22-34. DOI: 10.1109/TNNLS.2012.2222925.   X X  
8. Budka, M., 2013. Clustering as an example of optimizing arbitrarily chosen objective functions. In: Nguyen, N. T., Trawinski, B., Katarzyniak, R. and Jo, G.-S., eds. Advanced Methods for Computational Collective Intelligence (457). Springer Berlin / Heidelberg, pp. 177-186. DOI: 10.1007/978-3-642-34300-1_17.   X X X
9. Budka, M., Juszczyszyn, K., Musial, K. and Musial, A., 2013. Molecular Model of Dynamic Social Network Based on E-mail communication. Social Network Analysis and Mining, 3 (3), pp. 543-563. DOI: 10.1007/s13278-013-0101-4.       X
10. Fay, D., Kunegis, J. and Yoneki, E., 2013. Centrality and mode detection in dynamic contact graphs; a joint diagonalisation approach. ASONAM, pp. 41-48. DOI: 10.1145/2492517.2492540.       X
11. Howlett, R. J., B. Gabrys, K. Musial-Gabrys and J. Roach (Eds.),, 2013. Innovation through Knowledge Transfer 2012. Proceedings of the InnovationKT'2012 Conference, Smart Innovation, Systems and Technologies Series, Springer Series on Smart Innovation, Systems and Technologies, (18), ISBN 978-3-642-34219-6. DOI: 10.1007/978-3-642-34219-6.   X X X
12. Le, M., Nauck, D., Gabrys, B. and Martin, T., 2013. KNNs and Sequence Alignment for Churn Prediction. In: Bramer, M. and Petridis, M., eds. Research and Development in Intelligent Systems XXX. Springer International Publishing, pp. 279-285. DOI: 10.1007/978-3-319-02621-3_21.   X   X
13. Lemke, C., Budka, M. and Gabrys, B., 2013. Metalearning: a survey of trends and technologies. Artificial Intelligence Review, , pp. 1-14. DOI: 10.1007/s10462-013-9406-y.     X  
14. Lemke, C., Riedel, S. and Gabrys, B., 2013. Evolving forecast combination structures for airline revenue management. Journal of Revenue & Pricing Management, 12 (3), pp. 221-234. DOI: 10.1057/rpm.2012.30.   X X X
15. Musial, K., Gabrys, B. and Buczko, M., 2013. What kind of network are you? - Using local and global characteristics in network categorisation tasks. Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on, pp. 1366-1373.       X
16. Musial, K., Budka, M. and Juszczyszyn, K., 2013. Creation and growth of online social network. World Wide Web, 16 (4), pp. 421-447. DOI: 10.1007/s11280-012-0177-1.       X
17. Tsakonas, A. and Gabrys, B., 2013. A fuzzy evolutionary framework for combining ensembles. Applied Soft Computing, 13 (4), pp. 1800-1812. DOI: 10.1016/j.asoc.2012.12.027.   X X X
18. Stahl, F., Gabrys, B., Gaber, M. M. and Berendsen, M., 2013. An overview of interactive visual data mining techniques for knowledge discovery. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3 (4), pp. 239-256. DOI: 10.1002/widm.1093. X X   X
19. Pechenizkiy, M. and Žliobaitė, I., 2013. Introduction to the special issue on handling concept drift in adaptive information systems. Evolving Systems, 4 (1), pp. 1-2. DOI: 10.1007/s12530-012-9070-5. X X   X
 

2012

S A M C
1. Apeh, E., Žliobaitė, I., Pechenizkiy, M. and Gabrys, B., 2012. Predicting Multi-class Customer Profiles Based on Transactions: a Case Study in Food Sales. In: Bramer, M. and Petridis, M., eds. Research and Development in Intelligent Systems XXIX. Springer London, pp. 213-218. DOI: 10.1007/978-1-4471-4739-8_17.     X X
2. Balaguer-Ballester, E., Bouchachia, A., Jiang, B. and Denham, S., 2012. Neurodynamical Top-Down Processing during Auditory Attention. In: Huang, T., Zeng, Z., Li, C. and Leung, C., eds. Neural Information Processing (7664). Springer Berlin Heidelberg, pp. 266-273. DOI: 10.1007/978-3-642-34481-7_33.       X
3. Bouchachia, A., 2012. Dynamic Clustering. Evolving Systems, 3 (3), pp. 133-134. DOI: 10.1007/s12530-012-9062-5.   X X X
4. Bourouis, A., Feham, M. and Bouchachia, A., 2012. A New Architecture of a Ubiquitous Health Monitoring System: A Prototype Of Cloud Mobile Health Monitoring System. CoRR, abs/1205.6910.       X
5. Budka, M., Musial, K. and Juszczyszyn, K., 2012. Predicting the Evolution of Social Networks: Optimal Time Window Size for Increased Accuracy. 2012 ASE/IEEE International Conference on Social Computing (SocialCom 2012), pp. 21-30. DOI: 10.1109/SocialCom-PASSAT.2012.11.       X
6. Eastwood, M. and Gabrys, B., 2012. Generalised Bottom-up Pruning: A Model Level Combination of Decision Trees. Expert Syst. Appl., 39 (10), pp. 9150-9158. DOI: 10.1016/j.eswa.2012.02.061.       X
7. Hyman, J. M., Ma, L., Balaguer-Ballester, E., Durstewitz, D. and Seamans, J. K., 2012. Contextual encoding by ensembles of medial prefrontal cortex neurons. Proceedings of the National Academy of Sciences, 109 (13), pp. 5086-5091. DOI: 10.1073/pnas.1114415109.       X
8. Juszczyszyn, K., Musiał, K., Kazienko, P. and Gabrys, B., 2012. Temporal changes in local topology of an email-based social network. Computing and Informatics, 28 (6), pp. 763-779.       X
9. Juszczyszyn, K., Gonczarek, A., Tomczak, J., Musial, K. and Budka, M., 2012. A Probabilistic Approach to Structural Change Prediction in Evolving Social Networks. Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference on, pp. 996-1001. DOI: 10.1109/ASONAM.2012.173.       X
10. Le, M., Gabrys, B. and Nauck, D., 2012. A Hybrid Model for Business Process Event Prediction. In: Bramer, M. and Petridis, M., eds. Research and Development in Intelligent Systems XXIX. Springer London, pp. 179-192. DOI: 10.1007/978-1-4471-4739-8_13.   X    
11. Musial, K., Juszczyszyn, K. and Budka, M., 2012. Triad transition probabilities characterize complex networks. Awareness Magazine, . DOI: 10.2417/3201209.004369.       X
12. Pohl, D., Bouchachia, A. and Hellwagner, H., 2012. Automatic Sub-event Detection in Emergency Management Using Social Media. Proceedings of the 21st International Conference Companion on World Wide Web. ACM, pp. 683-686. DOI: 10.1145/2187980.2188180.       X
13. Stahl, F. and Bramer, M., 2012. Computationally efficient induction of classification rules with the PMCRI and J-PMCRI frameworks. Knowledge-Based Systems , 35 (0), pp. 49-63. DOI: 10.1016/j.knosys.2012.04.014.       X
14. Stahl, F., Gaber, M., Aldridge, P., May, D., Liu, H., Bramer, M. and Yu, P., 2012. Homogeneous and Heterogeneous Distributed Classification for Pocket Data Mining. In: Hameurlain, A., Küng, J. and Wagner, R., eds. Transactions on Large-Scale Data- and Knowledge-Centered Systems V (7100). Springer Berlin Heidelberg, pp. 183-205. DOI: 10.1007/978-3-642-28148-8_8.   X   X
15. Stahl, F. and Jordanov, I., 2012. An overview of the use of neural networks for data mining tasks. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2 (3), pp. 193-208. DOI: 10.1002/widm.1052.       X
16. Stahl, F. and Bramer, M., 2012. Jmax-pruning: A Facility for the Information Theoretic Pruning of Modular Classification Rules. Know.-Based Syst., 29, pp. 12-19. DOI: 10.1016/j.knosys.2011.06.016.       X
17. Stahl, F., Gaber, M. and Salvador, M., 2012. eRules: A Modular Adaptive Classification Rule Learning Algorithm for Data Streams. In: Bramer, M. and Petridis, M., eds. Research and Development in Intelligent Systems XXIX. Springer London, pp. 65-78. DOI: 10.1007/978-1-4471-4739-8_5.   X    
18. Tsakonas, A. and Gabrys, B., 2012. GRADIENT: Grammar-driven genetic programming framework for building multi-component, hierarchical predictive systems . Expert Systems with Applications , 39 (18), pp. 13253-13266. DOI: 10.1016/j.eswa.2012.05.076.       X
19. Tsakonas, A. and Gabrys, B., 2012. Fuzzy Base Predictor Outputs as Conditional Selectors for Evolved Combined Prediction System. IJCCI'12, pp. 34-41.   X   X
20. Žliobaitė, I., Bifet, A., Gaber, M., Gabrys, B., Gama, J., Minku, L. and Musial, K., 2012. Next Challenges for Adaptive Learning Systems. SIGKDD Explor. Newsl., 14 (1), pp. 48-55. DOI: 10.1145/2408736.2408746.   X    
21. Gabrys, B., 2012. Changing nature of research and business in view of the explosion of available data. Extended abstract of invited talk appearing in the proceedings of the e-Research South sponsored workshop on Smarter Research Management, Bournemouth, April 2012. X X X X
 

2011

S A M C
1. Tsakonas, A. and Gabrys, B., 2011. Evolving Takagi-Sugeno-Kang fuzzy systems using multi-population grammar guided genetic programming. International Conference on Evolutionary Computation Theory and Applications, ECTA 2011, pp. 278-281.   X   X
2. Budka, M., Gabrys, B. and Musial, K., 2011. On Accuracy of PDF Divergence Estimators and Their Applicability to Representative Data Sampling. Entropy, 13 (7), pp. 1229-1266. DOI: 10.3390/e13071229.   X   X
3. Eastwood, M. and Gabrys, B., 2011. Model level combination of tree ensemble hyperboxes via GFMM. Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on (1), pp. 443-447. DOI: 10.1109/FSKD.2011.6019563.       X
4. Jeary, S., Musial, K. and Phalp, K., 2011. Exploring the requirements process for a complex, adaptive system in a high risk software development environment. 18th EuroSPI Conference, 2011, Denmark., pp. 1-9. X     X
5. Juszczyszyn, K., Budka, M. and Musial, K., 2011. The Dynamic Structural Patterns of Social Networks Based on Triad Transitions. Advances in Social Networks Analysis and Mining (ASONAM), 2011 International Conference on, pp. 581-586. DOI: 10.1109/ASONAM.2011.50.       X
6. Kadlec, P. and Gabrys, B., 2011. Local learning-based adaptive soft sensor for catalyst activation prediction. AIChE Journal, 57 (5), pp. 1288-1301. DOI: 10.1002/aic.12346. X X   X
7. Kadlec, P., Grbić, R. and Gabrys, B., 2011. Review of adaptation mechanisms for data-driven soft sensors. Computers & Chemical Engineering, 35 (1), pp. 1-24. DOI: 10.1016/j.compchemeng.2010.07.034. X X    
7. Žliobaitė, I., 2011. Controlled Permutations for Testing Adaptive Classifiers. In: Elomaa, T., Hollmén, J. and Mannila, H., eds. Discovery Science (6926). Springer Berlin Heidelberg, pp. 365-379. DOI: 10.1007/978-3-642-24477-3_29.   X    
9. Žliobaitė, I., 2011. Identifying Hidden Contexts in Classification. In: Huang, J., Cao, L. and Srivastava, J., eds. Advances in Knowledge Discovery and Data Mining (6634). Springer Berlin Heidelberg, pp. 277-288. DOI: 10.1007/978-3-642-20841-6_23.   X    
10. Žliobaitė, I., 2011. Three Data Partitioning Strategies for Building Local Classifiers. In: Okun, O., Valentini, G. and Re, M., eds. Ensembles in Machine Learning Applications (373). Springer Berlin Heidelberg, pp. 233-250. DOI: 10.1007/978-3-642-22910-7_14.   X    
11. Žliobaitė, I., Bifet, A., Holmes, G. and Pfahringer, B., 2011. MOA Concept Drift Active Learning Strategies for Streaming Data. Proceedings of the Second Workshop on Applications of Pattern Analysis, JMLR Workshop and Conference Proceedings (17), pp. 48-55.   X    
12. Žliobaitė, I., Bifet, A., Pfahringer, B. and Holmes, G., 2011. Active Learning with Evolving Streaming Data. In: Gunopulos, D., Hofmann, T., Malerba, D. and Vazirgiannis, M., eds. Machine Learning and Knowledge Discovery in Databases (6913). Springer Berlin Heidelberg, pp. 597-612. DOI: 10.1007/978-3-642-23808-6_39.   X    
 

Previous group’s publications informing the original INFER project proposal

       
1. Budka, M. and Gabrys, B., 2010. Correntropy-based density-preserving data sampling as an alternative to standard cross-validation. Neural Networks (IJCNN), The 2010 International Joint Conference on, pp. 1-8. DOI: 10.1109/IJCNN.2010.5596717.        
2. Budka, M. and Gabrys, B., 2010. Ridge regression ensemble for toxicity prediction. Procedia Computer Science, 1 (1), pp. 193-201. DOI: 10.1016/j.procs.2010.04.022.        
3. Budka, M., 2010. Physically inspired methods and development of data–driven predictive systems. PhD Thesis (PhD). Bournemouth University, UK.        
4. Budka, M., Gabrys, B. and Ravagnan, E., 2010. Robust predictive modelling of water pollution using biomarker data. Water Research, 44 (10), pp. 3294-3308. DOI: 10.1016/j.watres.2010.03.006.        
5. Kadlec, P. and Gabrys, B., 2010. Adaptive on-line prediction soft sensing without historical data. Neural Networks (IJCNN), The 2010 International Joint Conference on, pp. 1-8. DOI: 10.1109/IJCNN.2010.5596965.        
6. Lemke, C. and Gabrys, B., 2010. Meta-learning for time series forecasting and forecast combination . Neurocomputing , 73 (10–12), pp. 2006-2016. DOI: 10.1016/j.neucom.2009.09.020.        
7. Lemke, C. and Gabrys, B., 2010. Meta-learning for time series forecasting in the NN GC1 competition. Fuzzy Systems (FUZZ), 2010 IEEE International Conference on, pp. 1-5. DOI: 10.1109/FUZZY.2010.5584001.        
8. Kadlec, P. and Gabrys, B., 2009. Architecture for development of adaptive on-line prediction models. Memetic Computing, 1 (4), pp. 241-269. DOI: 10.1007/s12293-009-0017-8.        
9. Kadlec, P. and Gabrys, B., 2009. Evolving on-line prediction model dealing with industrial data sets. Evolving and Self-Developing Intelligent Systems, 2009. ESDIS '09. IEEE Workshop on, pp. 24-31. DOI: 10.1109/ESDIS.2009.4938995.        
10. Kadlec, P. and Gabrys, B., 2009. Soft Sensor Based on Adaptive Local Learning. Proceedings of the 15th International Conference on Advances in Neuro-information Processing - Volume Part I. Springer-Verlag, pp. 1172-1179.        
11. Kadlec, P., 2009. On robust and adaptive soft sensors. PhD Thesis (PhD). Bournemouth University, UK.        
12. Kadlec, P., Gabrys, B. and Strandt, S., 2009. Data-driven Soft Sensors in the process industry. Computers & Chemical Engineering , 33 (4), pp. 795-814. DOI: 10.1016/j.compchemeng.2008.12.012.        
13. Lemke, C., Riedel, S. and Gabrys, B., 2009. Dynamic combination of forecasts generated by diversification procedures applied to forecasting of airline cancellations. Computational Intelligence for Financial Engineering, 2009. CIFEr '09. IEEE Symposium on, pp. 85-91. DOI: 10.1109/CIFER.2009.4937507.        
14. Kadlec, P. and Gabrys, B., 2008. Adaptive Local Learning Soft Sensor for Inferential Control Support. Computational Intelligence for Modelling Control Automation, 2008 International Conference on, pp. 243-248. DOI: 10.1109/CIMCA.2008.66.        
15. Kadlec, P. and Gabrys, B., 2008. Application of Computational Intelligence Techniques to Process Industry Problems. In: Nguyen, N.T., Kolaczek, G. and Gabrys, B., eds. Knowledge Processing and Reasoning for Information Society. EXIT Publishing House, pp. 305-322.        
16. Bouchachia, A., Gabrys, B. and Sahel, Z., 2007. Overview of Some Incremental Learning Algorithms. Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007. IEEE International, pp. 1-6. DOI: 10.1109/FUZZY.2007.4295640.        
17. Eastwood, M. and Gabrys, B., 2007. The Dynamics of Negative Correlation Learning. The Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology, 49 (2), pp. 251-263. DOI: 10.1007/s11265-007-0074-5.        
18. Kadlec, P. and Gabrys, B., 2007. Nature-Inspired Adaptive Architecture for Soft Sensor Modelling. NiSIS'2007 Symposium: 3rd European Symposium on Nature-inspired Smart Information Systems, 26- 27 November 2007, St Julian's, Malta.        
19. Riedel, S. and Gabrys, B., 2007. Combination of Multi Level Forecasts. The Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology, 49 (2), pp. 265-280. DOI: 10.1007/s11265-007-0076-3.        
20. Riedel, S. and Gabrys, B., 2007. Dynamic Pooling for the Combination of Forecasts generated using Multi Level Learning. Neural Networks, 2007. IJCNN 2007. International Joint Conference on, pp. 454-459. DOI: 10.1109/IJCNN.2007.4370999.        
21. Ruta, D. and Gabrys, B., 2007. Neural Network Ensembles for Time Series Prediction. Neural Networks, 2007. IJCNN 2007. International Joint Conference on, pp. 1204-1209. DOI: 10.1109/IJCNN.2007.4371129.        
22. Gabrys, B. and Ruta, D., 2006. Genetic algorithms in classifier fusion. Applied Soft Computing, 6 (4), pp. 337-347. DOI: 10.1016/j.asoc.2005.11.001.        
23. Ruta, D. and Gabrys, B., 2005. Classifier selection for majority voting. Information Fusion , 6 (1), pp. 63-81. DOI: 10.1016/j.inffus.2004.04.008.        
24. Gabrys, B. and Petrakieva, L., 2004. Combining labelled and unlabelled data in the design of pattern classification systems. International Journal of Approximate Reasoning, 35 (3), pp. 251-273. DOI: 10.1016/j.ijar.2003.08.005.        
25. Gabrys, B., 2004. Learning hybrid neuro-fuzzy classifier models from data: to combine or not to combine? Fuzzy Sets and Systems, 147 (1), pp. 39-56. DOI: 10.1016/j.fss.2003.11.010.        
26. Gabrys, B., 2002. Agglomerative Learning Algorithms for General Fuzzy Min-Max Neural Network. J. VLSI Signal Process. Syst., 32 (1/2), pp. 67-82. DOI: 10.1023/A:1016315401940.        
27. Gabrys, B., 2002. Neuro-fuzzy approach to processing inputs with missing values in pattern recognition problems. International Journal of Approximate Reasoning, 30 (3), pp. 149-179. DOI: 10.1016/S0888-613X(02)00070-1.        
28. Ruta, D. and Gabrys, B., 2002. A Theoretical Analysis of the Limits of Majority Voting Errors for Multiple Classifier Systems. Pattern Analysis & Applications, 5 (4), pp. 333-350. DOI: 10.1007/s100440200030.        
29. Ruta, D. and Gabrys, B., 2001. Analysis of the Correlation between Majority Voting Error and the Diversity Measures in Multiple Classifier Systems. Proceedings of the SOCO/ISFI'2001 Conference, Paisley, UK, 2001.        
30. Ruta, D. and Gabrys, B., 2001. Application of the Evolutionary Algorithms for Classifier Selection in Multiple Classifier Systems with Majority Voting. In: Kittler, J. and Roli, F., eds. Multiple Classifier Systems (2096). Springer Berlin Heidelberg, pp. 399-408. DOI: 10.1007/3-540-48219-9_40.        
31. Gabrys, B. and Bargiela, A., 2000. General fuzzy min-max neural network for clustering and classification. Neural Networks, IEEE Transactions on, 11 (3), pp. 769-783. DOI: 10.1109/72.846747.        
32. Ruta, D. and Gabrys, B., 2000. An overview of classifier fusion methods. Computing and Information systems, 7 (1), pp. 1-10.        
33. Gabrys, B. and Bargiela, A., 1999. Neural Networks Based Decision Support in Presence of Uncertainties. Journal of Water Resources Planning and Management, 125 (5), pp. 272-280. DOI: 10.1061/(ASCE)0733-9496(1999)125:5(272).