Bogdan Gabrys

Professor Bogdan Gabrys

  • 01202 965298
  • bgabrys at bournemouth dot ac dot uk
  • Professor 2
  • Poole House P256a, Talbot Campus, Fern Barrow, Poole, BH12 5BB
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Biography

Bogdan Gabrys is a Data Scientist and a Chair in Computational Intelligence at the Faculty of Science and Technology, Bournemouth University, UK.

After many years of working at different Universities Prof Gabrys moved to the Bournemouth University in January 2003 where he has founded and acted as a Head of Data Science Institute, a Director of the SMART Technology Research Centre and a Head of the Computational Intelligence Research Group within the School of Design, Engineering & Computing and the Faculty of Science and Technology.

His research, consulting and advisory activities have concentrated on the areas of data science, complex adaptive systems, computational intelligence, machine learning, predictive analytics and their diverse applications. In particular, he has pursued the development of various statistical, machine learning, nature inspired and hybrid intelligent techniques especially targeting data and information fusion, learning and adaptive methods, multiple classifier and prediction systems, processing and modelling of uncertainty in pattern recognition, diagnostic analysis and decision support systems...

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Journal Articles

  • Bakirov, R., Gabrys, B. and Fay, D., 2017. Multiple adaptive mechanisms for data-driven soft sensors. Computers and Chemical Engineering, 96, 42-54.
  • Vaughan, N., Gabrys, B. and Dubey, V.N., 2016. An overview of self-adaptive technologies within virtual reality training. Computer Science Review, 22, 65-87.
  • Wang, W., Jiao, P., He, D., Jin, D., Pan, L. and Gabrys, B., 2016. Autonomous overlapping community detection in temporal networks: A dynamic Bayesian nonnegative matrix factorization approach. Knowledge-Based Systems, 110, 121-134.
  • Al-Jubouri, B. and Gabrys, B., 2016. Local Learning for Multi-layer, Multi-component Predictive System. Procedia Computer Science, 96, 723-732.
  • Salvador, M.M., Budka, M. and Gabrys, B., 2016. Effects of Change Propagation Resulting from Adaptive Preprocessing in Multicomponent Predictive Systems. Procedia Computer Science, 96, 713-722.
  • Vaughan, N. and Gabrys, B., 2016. Comparing and Combining Time Series Trajectories Using Dynamic Time Warping. Procedia Computer Science, 96, 465-474.
  • Jin, D., Gabrys, B. and Dang, J., 2015. Combined node and link partitions method for finding overlapping communities in complex networks. Scientific Reports, 5.
  • Lemke, C., Budka, M. and Gabrys, B., 2015. Metalearning: a survey of trends and technologies. Artificial Intelligence Review, 44 (1), 117-130.
  • Le, M., Gabrys, B. and Nauck, D., 2014. A hybrid model for business process event and outcome prediction. Expert Systems.
  • Apeh, E., Gabrys, B. and Schierz, A., 2014. Customer profile classification: To adapt classifiers or to relabel customer profiles? Neurocomputing, 132, 3-13.
  • Zliobaite, I. and Gabrys, B., 2014. Adaptive Preprocessing for Streaming Data. IEEE Transactions on Knowledge and Data Engineering, 26 (2), 309-321.
  • Arsene, C.T.C. and Gabrys, B., 2014. Mixed simulation-state estimation of water distribution systems based on a least squares loop flows state estimator. Applied Mathematical Modelling, 38 (2), 599-619.
  • Arsene, C.T.C. and Gabrys, B., 2013. Probabilistic finite element predictions of the human lower limb model in total knee replacement. Medical Engineering and Physics, 35 (8), 1116-1132.
  • 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), 239-256.
  • Apeh, E. and Gabrys, B., 2013. Detecting and Visualizing the Change in Classification of Customer Profiles based on Transactional Data. Evolving Systems, 4 (1), 27-42.
  • Tsakonas, A. and Gabrys, B., 2013. A fuzzy evolutionary framework for combining ensembles. Applied Soft Computing Journal, 13 (4), 1800-1812.
  • Kadlec, P. and Gabrys, B., 2013. Erratum to Architecture for development of adaptive on-line prediction models(Memetic Comp., (2009), 1, (241-269), 10.1007/s12293-009-0017-8). Memetic Computing, 5 (1), 79.
  • Arsene, C.T.C., Gabrys, B. and Al-Dabass, D., 2012. Decision support system for water distribution systems based on neural networks and graphs theory for leakage detection. Expert Systems with Applications, 39 (18), 13214-13224.
  • 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), 13253-13266.
  • Zliobaite, I., Bifet, A., Gaber, M.M., Gabrys, B., Gama, J., Minku, L.L. and Musial, K., 2012. Next challenges for adaptive learning systems. SIGKDD Explorations, 14, 48-55.
  • Eastwood, M. and Gabrys, B., 2012. Generalised bottom-up pruning: A model level combination of decision trees. Expert Syst. Appl., 39, 9150-9158.
  • Khan, L., Gabrys, B. et al., 2011. Preface. Proceedings - IEEE International Conference on Data Mining, ICDM.
  • Eastwood, M. and Gabrys, B., 2011. Model level combination of tree ensemble hyperboxes via GFMM. Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, 1, 443-447.
  • Kadlec, P. and Gabrys, B., 2011. Local learning-based adaptive soft sensor for catalyst activation prediction. AIChE Journal, 57, 1288-1301.
  • Budka, M. and Gabrys, B., 2011. Electrostatic field framework for supervised and semi–supervised learning from incomplete data. Natural Computing, 10, 921-945.
  • Lemke, C., Riedel, S. and Gabrys, B., 2011. Evolving forecast combination structures for airline revenue management. Journal of Revenue and Pricing Management.
  • Budka, M., Gabrys, B. and Musial, K., 2011. On accuracy of PDF divergence estimators and their applicability to representative data sampling. Entropy, 13, 1229-1266.
  • Kadlec, P., Grbic, R. and Gabrys, B., 2011. Review of adaptation mechanisms for data-driven soft sensors. Computers and Chemical Engineering, 35, 1-24.
  • Pampanin, D.M., Budka, M., Gabrys, B. et al., 2010. The Marine Environment I.Q. concept. Developing an Index of the Quality of the Marine Environment based on biomarkers: integration of pollutant effects on marine organisms. Comparative Biochemistry and Physiology - Part A: Molecular & Integrative Physiology, S52.
  • Budka, M. and Gabrys, B., 2010. Ridge regression ensemble for toxicity prediction. Procedia Computer Science, 1, 193-201.
  • Budka, M., Gabrys, B. and Ravagnan, E., 2010. Robust predictive modelling of water pollution using biomarker data. Water Research, 44, 3294-3308.
  • Ruta, D., Gabrys, B. and Lemke, C., 2010. A Generic Multilevel Architecture for Time Series Prediction. IEEE Transactions on Knowledge and Data Engineering, 99.
  • Howlett, R.J., Lovrek, I., Jain, L.C., Lim, C.-P. and Gabrys, B., 2010. Advances in design and application of neural networks. Neural Computing and Applications (A special edition), 1.
  • Lemke, C. and Gabrys, B., 2010. Meta-learning for time series forecasting and forecast combination. Neurocomputing, 73, 2006-2016.
  • Kadlec, P., Gabrys, B. and Strandt, S., 2009. Data-driven Soft Sensors in the Process Industry. Computers and Chemical Engineering, 33, 795-814.
  • Gabrys, B. and Anguita, D., 2009. Nature-inspired Learning and Adaptive Systems. Natural Computing, 8, 197-198.
  • Ruta, D. and Gabrys, B., 2009. A Framework for Machine Learning Based on Dynamic Physical Fields. Natural Computing, 8, 219-237.
  • Kadlec, P. and Gabrys, B., 2009. Architecture for development of adaptive on-line prediction models. Memetic Computing, 1, 241-269.
  • Riedel, S. and Gabrys, B., 2009. Pooling for Combination of Multi Level Forecasts. IEEE Transactions on Knowledge and Data Engineering, 21, 1753-1766.
  • Juszczyszyn, K., Musial, K., Kazienko, P. and Gabrys, B., 2009. Temporal changes in local topology of an email-based social network. Computing and Informatics, 28, 763-779.
  • Riedel, S. and Gabrys, B., 2007. Combination of Multi Level Forecasts. Journal of VLSI Signal Processing Systems, 49, 265-280.
  • Liu, H., Howlett, R.J. and Gabrys, B., 2007. Special Issue: Extended Papers Selected from KES-2006. International Journal of Knowledge-Based Intelligent Engineering Systems, 11, 199-200.
  • Eastwood, M. and Gabrys, B., 2007. The Dynamics of Negative Correlation Learning. Journal of VLSI Signal Processing Systems, 49, 251-263.
  • Gabrys, B. and Ruta, D., 2006. Genetic algorithms in classifier fusion. Applied Soft Computing, 6, 337-347.
  • Ruta, D. and Gabrys, B., 2005. Classifier selection for majority voting. Information Fusion, 6, 63-81.
  • Gabrys, B., 2004. Learning hybrid neuro-fuzzy classifier models from data: to combine or not to combine? Fuzzy Sets and Systems, 147, 39-56.
  • Gabrys, B., 2004. Special Issue on Integration of Methods and Hybrid Systems. International Journal of Approximate Reasoning, 35, 203-204.
  • Gabrys, B. and Petrakieva, L., 2003. Combining labelled and unlabelled data in the design of pattern classification systems. International Journal of Approximate Reasoning, 35, 251-273.
  • Ruta, D. and Gabrys, B., 2003. Physical field models for pattern classification. Soft Computing, 8, 126-141.
  • Gabrys, B., 2002. Neuro-fuzzy approach to processing inputs with missing values in pattern recognition problems. International Journal of Approximate Reasoning, 30, 149-179.
  • Ruta, D. and Gabrys, B., 2002. A theoretical analysis of the limits of majority voting errors for multiple classifier systems. Pattern Analysis and Applications, 5, 333-350.
  • Gabrys, B., 2002. Agglomerative learning algorithms for general fuzzy min-max neural network. Journal of VLSI Signal Processing Systems, 32, 67-82.
  • Fyfe, C. and Gabrys, B., 2002. Guest Editorial Introduction. Knowledge-Based Systems, 15, 1-2.
  • Ruta, D. and Gabrys, B., 2001. The Boundaries of Knowledge. Computing and Information Systems, 8, 109-110.
  • Gabrys, B. and Bargiela, A., 2000. General fuzzy min-max neural network for clustering and classification. Neural Networks, IEEE Transactions on, 11, 769-783.
  • Ruta, D. and Gabrys, B., 2000. An Overview of Classifier Fusion Methods. Computing and Information Systems, 7, 1-10.
  • Gabrys, B. and Bargiela, A., 1999. Analysis of Uncertainties in Water Systems Using Neural Networks. Measurement + Control, 32, 145-147.
  • Gabrys, B. and Bargiela, A., 1999. Neural Networks Based Decision Support in Presence of Uncertainties. Journal of Water Resources Planning and Management, 125, 272-280.
  • Salvador, M.M., Budka, M. and Gabrys, B.. Automatic composition and optimisation of multicomponent predictive systems.

Books

  • Howlett, R.J., Gabrys, B., Musial-Gabrys, K. and Roach, J., 2013. Innovation through Knowledge Transfer 2012.
  • Knowledge Processing and Reasoning for Information Society. Warsaw, Poland: EXIT Publishing House.
  • Knowledge-based intelligent information and engineering systems : 10th international conference, KES 2006, Bournemouth, UK, October 9-11, 2006 ; proceedings. Springer-Verlag.
  • Knowledge-Based Intelligent Information and Engineering Systems 10th International Conference, KES 2006, Bournemouth, UK, October 9-11, 2006. Proceedings, Part I. Springer Berlin / Heidelberg.
  • Knowledge-Based Intelligent Information and Engineering Systems 10th International Conference, KES 2006, Bournemouth, UK, October 9-11, 2006. Proceedings, Part II. Springer Berlin / Heidelberg.
  • Knowledge-Based Intelligent Information and Engineering Systems 10th International Conference, KES 2006, Bournemouth, UK, October 9-11, 2006. Proceedings, Part III. Springer Berlin / Heidelberg.
  • Do Smart Adaptive Systems Exist? - Best Practice for Selection and Combination of Intelligent Methods. Springer Berlin / Heidelberg.

Chapters

  • Howlett, R.J. and Gabrys, B., 2013. InnovationKT-2012 Preface.
  • Korsunsky, A.M., Gabrys, B. et al., 2010. WCE 2010 - World Congress on Engineering 2010: Preface.
  • Ahmad, M., Gabrys, B. et al., 2010. WCE 2010 - World Congress on Engineering 2010: Preface.
  • Eastwood, M. and Gabrys, B., 2008. Building Combined Classifiers. In: Nguyen, N.T., Kolaczek, G. and Gabrys, B., eds. Knowledge Processing and Reasoning for Information Society. Warsaw, Poland: EXIT Publishing House, 139-163.
  • 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. Warsaw, Poland: EXIT Publishing House, 305-322.
  • Gabrys, B., Jones, M. and Polkinghorne, M., 2008. Commercial application of computational intelligence - A Knowledge Transfer Case Study. Applications of knowledge transfer to small and medium sized businesses. 2008 ed.. Poole, England: Bournemouth University.
  • Musial, K., Kazienko, P. and Kajdanowicz, T., 2008. Multirelational Social Networks in Multimedia Sharing Systems. In: Nguyen, N.T., Kolaczek, G. and Gabrys, B., eds. Knowledge Processing and Reasoning for Information Society. Academic Publishing House EXIT, 275-292.
  • Kadlec, P. and Gabrys, B., 2008. Gating Artificial Neural Network Based Soft Sensor. In: Nguyen, N.T. and Katarzyniak, R., eds. New Challenges in Applied Intelligence Technologies. Berlin: Springer-Verlag, 193-202.
  • Lemke, C. and Gabrys, B., 2008. Forecasting and Forecast Combination in Airline Revenue Management Applications. In: Nguyen, N.T., Kolaczek, G. and Gabrys, B., eds. Knowledge Processing and Reasoning for Information Society. Warsaw, Poland: EXIT Publishing House, 231-247.
  • Howlett, B., Gabrys, B. and Jain, L., 2006. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics: Preface.
  • Gabrys, B., 2005. Do smart adaptive systems exist? Introduction. In: Gabrys, B., Leiviskä, K. and Strackeljan, J., eds. Do Smart Adaptive Systems Exist? Best Practice for Selection and Combination of Intelligent Methods. Berlin: Springer, 1-17.
  • Gabrys, B. and Petrakieva, L., 2004. Selective sampling for combined learning from labelled and unlabelled data. In: Lotfi, A. and Garibaldi, J.M., eds. Applications and science in soft computing. London: Springer, 139-148.
  • Ruta, D. and Gabrys, B., 2003. Set analysis of coincident errors and its applications for combining classifiers. In: Chen, D. and Cheng, X., eds. Pattern Recognition and String Matching. Dordrecht; Boston: Kluwer Academic, 647-672.

Conferences

  • He, H., Tiwari, A., Mehnen, J., Watson, T., Maple, C., Jin, Y. and Gabrys, B., 2016. Incremental information gain analysis of input attribute impact on RBF-kernel SVM spam detection. 1022-1029.
  • He, H., Maple, C., Watson, T., Tiwari, A., Mehnen, J., Jin, Y. and Gabrys, B., 2016. The security challenges in the IoT enabled cyber-physical systems and opportunities for evolutionary computing & other computational intelligence. 1015-1021.
  • Bakirov, R., Gabrys, B. and Fay, D., 2016. Augmenting adaptation with retrospective model correction for non-stationary regression problems. 771-779.
  • Vaughan, N. and Gabrys, B., 2016. Comparing and combining time series trajectories using Dynamic Time Warping. In: 20th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems 5-7 September 2016 York, UK. Elsevier.
  • Al-Jubouri, B. and Gabrys, B., 2016. Local learning for multi-layer, multi-component predictive system. In: 20th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, KES2016 5-7 September 2016 York, United Kingdom.
  • Martin Salvador, M., Budka, M. and Gabrys, B., 2016. Towards automatic composition of multicomponent predictive systems. 27-39.
  • Bakirov, R., Gabrys, B. and Fay, D., 2015. On Sequences of Different Adaptive Mechanisms in Non-Stationary Regression Problems. In: 2015 International Joint Conference on Neural Networks 12-17 July 2015 Killarney, Ireland.
  • Al-Jubouri, B. and Gabrys, B., 2015. Multicriteria approaches for predictive model generation: A comparative experimental study. 64-71.
  • Budka, M., Eastwood, M., Gabrys, B., Kadlec, P., Martin Salvador, M., Schwan, S., Tsakonas, A. and Žliobaitė, I., 2014. From Sensor Readings to Predictions: On the Process of Developing Practical Soft Sensors. In: The Thirteenth International Symposium on Intelligent Data Analysis (IDA 2014) 30 October-1 November 2014 Leuven, Belgium. Springer, 49-60.
  • Salvador, M.M., Gabrys, B. and Žliobaitė, I., 2014. Online Detection of Shutdown Periods in Chemical Plants: A Case Study. 580 - 588.
  • Le, M., Nauck, D., Gabrys, B. and Martin, T., 2014. Sequential Clustering for Event Sequences and Its Impact on Next Process Step Prediction. 168-178.
  • Gabrys, B., 2014. Robust Adaptive Predictive Modeling and Data Deluge (Extended Abstract). 39-41.
  • Tsakonas, A. and Gabrys, B., 2014. Application of base learners as conditional input for fuzzy rule-based combined system. 19-32.
  • 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. 691-698.
  • Musial, K., Gabrys, B. and Buczko, M., 2013. What kind of network are you? - Using local and global characteristics in network categorisation tasks. 1366-1373.
  • Bakirov, R. and Gabrys, B., 2013. Investigation of Expert Addition Criteria for Dynamically Changing Online Ensemble Classifiers with Multiple Adaptive Mechanisms. Springer Berlin Heidelberg, 646-656.
  • Le, M., Nauck, D. and Gabrys, B., 2013. Sequential approaches for predicting business process outcome and process failure warning. 1-15.
  • Apeh, E., Žliobaite, I., Pechenizkiy, M. and Gabrys, B., 2012. Predicting multi-class customer profiles based on transactions: A case study in food sales. 213-218.
  • Le, M., Gabrys, B. and Nauck, D., 2012. A hybrid model for business process event prediction. 179-192.
  • Tsakonas, A. and Gabrys, B., 2012. Fuzzy Base Predictor Outputs as Conditional Selectors for Evolved Combined Prediction System. SciTePress, 34-41.
  • Tsakonas, A. and Gabrys, B., 2011. Evolving Takagi-Sugeno-Kang fuzzy systems using multi-population grammar guided genetic programming. In: International Conference on Evolutionary Computation Theory and Applications (ECTA'11) 24-26 October 2011 Paris, France. Paris: INSTICC.
  • Apeh, E.T., Gabrys, B. and Schierz, A.C., 2011. Customer profile classification using transactional data. IEEE, 37-43.
  • Apeh, E.T. and Gabrys, B., 2011. Change Mining of Customer Profiles Based on Transactional Data. IEEE Computer Society, 560-567.
  • Vaughan, N., Dubey, V.N., Wee, M.Y.K. and Gabrys, B., 2011. Simulating Epidural Injection: A virtual patient for training. In: SET for Britain 14 March 2011 House of Commons, London.
  • Kadlec, P. and Gabrys, B., 2010. Adaptive on-line prediction soft sensing without historical data. In: World Congress on Computational Intelligence (WCCI 2010) Barcelona, Spain..
  • Budka, M. and Gabrys, B., 2010. Correntropy–based density–preserving data sampling as an alternative to standard cross–validation. In: World Congress on Computational Intelligence (WCCI 2010) 18-23 July 2010 Barcelona, Spain. IEEE, 1-8.
  • Lemke, C. and Gabrys, B., 2010. Meta-learning for time series forecasting in the NN GC1 competition. In: World Congress on Computational Intelligence (WCCI 2010) Barcelona, Spain.
  • Kadlec, P. and Gabrys, B., 2009. Soft sensors: Where are we and what are the current and future challenges?
  • Lemke, C., Riedel, S. and Gabrys, B., 2009. Dynamic Combination of Forecasts Generated by Diversification Procedures Applied to Forecasting of Airline Cancellations. Nashville: IEEE, 85-91.
  • Budka, M. and Gabrys, B., 2009. Electrostatic Field Classifier for Deficient Data. Heidelberg: Springer, 311-318.
  • Kadlec, P. and Gabrys, B., 2009. Evolving on-line prediction model dealing with industrial data sets. Nashville: IEEE, 24-31.
  • Gabrys, B., 2009. Learning with Missing or Incomplete Data. Springer Berlin / Heidelberg, 1-4.
  • Eastwood, M. and Gabrys, B., 2009. A Non-Sequential Representation of Sequential Data for Churn Prediction. Heidelberg: Springer, 209-218.
  • Kadlec, P. and Gabrys, B., 2009. Self-Adapting Soft Sensor for On-Line Prediction. Heidelberg: Springer, 1172-1179.
  • Gabrys, B., 2009. Robust adaptive soft sensors for process industry - Keynote talk. In: International Workshop on Computational Intelligence in Security for Information Systems (CISIS'2009) 23-26 September 2009 Burgos, Spain.
  • Musial, K., Juszczyszyn, K., Gabrys, B. and Kazienko, P., 2009. Patterns of interactions in complex social networks based on coloured motifs analysis. 607-614.
  • Lemke, C. and Gabrys, B., 2008. On the Benefit of Using Time Series Features for Choosing a Forecasting Method. In: 2nd European Symposium on Time Series Prediction 17-19 September 2008 Porvoo, Finland.
  • Gabrys, B., 2008. Self-adapting architecture for building powerful predictive models - Keynote talk. In: The 15th International Conference on Neural Information Processing (ICONIP 2008) 24-27 November 2008 Auckland, New Zealand.
  • Juszczyszyn, K., Kazienko, P., Musial, K. and Gabrys, B., 2008. Temporal Changes in Connection Patterns of an Email-based Social Network. IEEE Press.
  • Kadlec, P. and Gabrys, B., 2008. Adaptive Local Learning Soft Sensor for Inferential Control Support. IEEE Computer Society, 243-248.
  • Kadlec, P. and Gabrys, B., 2008. Learnt Topology Gating Artificial Neural Networks. IEEE, 2604-2611.
  • Lemke, C. and Gabrys, B., 2008. Do We Need Experts for Time Series Forecasting? In: 16th European Symposium on Artificial Neural Networks (ESANN'2008) April 2008 Bruges, Belgium. d-side, 253-258.
  • Gabrys, B., 2008. Do Smart Adaptive Systems Exist? Hybrid Intelligent Systems Perspective - Keynote Talk. Springer Berlin / Heidelberg, 2-3.
  • Riedel, S. and Gabrys, B., 2007. Dynamic Pooling for the Combination of Forecasts Generated Using Multi Level Learning. In: Neural Networks, 2007. IJCNN 2007. International Joint Conference on 12-17 August 2007 Orlando, FL,. IEEE Press, 454-459.
  • Gabrys, B., 2007. Keynote Speech. In: World Congress on Engineering (WCE'2007) 2-4 July 2007 London. International Association of Engineers.
  • Gabrys, B., 2007. Keynote Speech. In: European Conference on Data Mining (ECDM'2007) 6-8 July 2007 Lisbon, Portugal.
  • Kadlec, P. and Gabrys, B., 2007. Nature-Inspired Adaptive Architecture for Soft Sensor Modelling. In: NiSIS'2007 Symposium: 3rd European Symposium on Nature-inspired Smart Information Systems 26-27 November 2007 St Julian's, Malta.
  • Ruta, D. and Gabrys, B., 2007. Neural Network Ensembles for Time Series Prediction. In: Neural Networks, 2007. IJCNN 2007. International Joint Conference on 12-17 August 2007 Orlando, FL,. IEEE Press, 1204-1209.
  • Sahel, Z., Bouchachia, A., Gabrys, B. and Rogers, P., 2007. Adaptive Mechanisms for Classification Problems with Drifting Data. Berlin: Springer, 419-426.
  • Lemke, C. and Gabrys, B., 2007. Review of Nature-Inspired Forecast Combination Techniques. In: NiSIS'2007 Symposium: 3rd European Symposium on Nature-inspired Smart Information Systems 26-27 November 2007 St Julian's, Malta.
  • Bouchachia, A., Gabrys, B. and Sahel, Z., 2007. Overview of Some Incremental Learning Algorithms. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'2007): Proceedings 23-26 July 2007 London. London, UK, July 2007, 1-6.
  • Macas, M.B., Gabrys, B., Ruta, D. and Lhotska, L., 2007. Particle Swarm Optimisation of Multiple Classifier Systems. Berlin: Springer, 333-340.
  • Ruta, D. and Gabrys, B., 2007. Reducing Spatial Data Complexity for Classification Models. Melville, N.Y.: American Institute of Physics, 603-613.
  • Gabrys, B., 2006. Do Smart Adaptive Systems Exist? - Invited Lecture. In: Engineering Research in Action (ERA) Day 13 June 2006 University of Brighton, England.
  • Gabrys, B., 2006. Do Smart Adaptive Systems Exist? - Keynote Talk. In: Workshop on Adaptive Systems - Advanced Issues 24 February 2006 Southampton, England.
  • Gabrys, B., 2006. Keynote Speech. In: International Scientists IT Workshop Series 13 November 2006 The Catholic University of Daegu, Daegu, South Korea.
  • Gabrys, B., 2006. Keynote Speech. In: International Scientists IT Workshop Series 14 November 2006 Hannam University, Daejeon, South Korea.
  • Gabrys, B., 2006. Keynote Speech. In: International Scientists IT Workshop Series 15 November 2006 Inha University, Incheon, South Korea.
  • Gabrys, B., 2006. Do Smart Adaptive Systems Exist? - Soft Computing Perspective - Keynote Talk. In: 6th International Conference on Recent Advances in Soft Computing (RASC'2006) 10-12 July 2006 University of Kent, Canterbury, England.
  • Gabrys, B., 2006. Do Smart Adaptive Systems Exist?- Hybrid Intelligent Systems Perspective - Invited Lecture. In: Summer School July 2006 University of Burgos, Burgos, Spain.
  • Gabrys, B., 2006. Do Smart Adaptive Systems Exist? - A hybrid intelligent systems perspective. - Keynote Talk. In: 2006 International Forum on a Life and Adaptive Robotics 8-9 November 2006 ITRC-Intelligent Robot Research Center at the Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.
  • Gabrys, B., 2006. Multiple Classifier Systems - Issues, Motivations and Challenges. In: Informatics Institute Seminar Talk 21 April 2006 University of Exeter, England.
  • Gabrys, B., 2006. Multiple Classifier Systems - Issues, Motivations and Challenges. In: School of DEC Seminar Series 18 January 2006 Bournemouth University, England.
  • Knowledge-Based Intelligent Information and Engineering Systems, 10th International Conference, KES 2006, Bournemouth, UK, October 9-11, 2006, Proceedings, Part II. Springer.
  • Knowledge-Based Intelligent Information and Engineering Systems, 10th International Conference, KES 2006, Bournemouth, UK, October 9-11, 2006, Proceedings, Part III. Springer.
  • Eastwood, M. and Gabrys, B., 2006. Lambda as a Complexity Control in Negative Correlation Learning. In: NiSIS'2006 Symposium : 2nd European Symposium on Nature-inspired Smart Information Systems 29 November-1 December 2006 Puerta de la Cruz, Tenerife, Spain.
  • Corchado, E., Baruque, B. and Gabrys, B., 2006. Maximum Likelihood Topology Preserving Ensembles. Springer Berlin / Heidelberg, 1434-1442.
  • Apeh, E. and Gabrys, B., 2006. Clustering for Data Matching. Berlin: Springer, 1216-1225.
  • Gabrys, B., 2006. To Combine or Not to Combine? - Multiple Classifier and Prediction Systems - Invited Lecture. In: Summer School July 2006 University of Burgos, Burgos, Spain.
  • Baruque, B., Corchado, E., Gabrys, B., Herrero, Á., Rovira, J. and Gonzalez, J., 2006. Unsupervised Ensembles Techniques for Visualization. In: NiSIS'2006 Symposium : 2nd European Symposium on Nature-inspired Smart Information Systems 29 November-1 December 2006 Puerta de la Cruz, Tenerife, Spain.
  • Gabrys, B., Baruque, B. and Corchado, E., 2006. Outlier Resistant PCA Ensembles. Berlin: Springer, 432-440.
  • Gabrys, B., 2006. Overview of Research Interests, Projects and Activities in the Computational Intelligence Research Group. In: School of DEC Seminars Series, 15 February 2006 Bournemouth Univeristy, England.
  • Gabrys, B., 2005. Hybrid Intelligent Methods and Smart Adaptive Systems. In: Invited Lecture, University of Burgos 15 April 2005 Burgos, Spain.
  • Ruta, D. and Gabrys, B., 2005. Nature-Inspired Learning Models. In: NiSIS'2005 (Nature-Inspired Smart Information Systems) Symposium 4-5 October 2005 Albufeira, Portugal.
  • Gabrys, B., 2005. Multilevel Classifier Systems - Issues, Motivations and Challenges - Invited Lecture. In: Talks at the Centre for Intelligent Agents and Multi-Agent Systems (CIAMAS) 5 September 2005 Swinburne University of Technology, Melbourne, Australia.
  • Gabrys, B., 2005. Multilevel Classifier Systems - Issues, Motivations and Challenges - Invited lecture. In: Monash Data Mining Centre (MDMC) 13 September 2005 Monash University, Melbourne, Australia.
  • Gabrys, B., 2005. Multilevel Classifier Systems - Issues, Motivations and Challenges - Invited Lecture. In: Talk at the Bioinformatics Applications Research Centre (BARC) 21 September 2005 James Cook University, Townsville, Australia.
  • Gabrys, B., 2005. Multilevel Prediction and Classification Systems. In: Invited Talk: University of Magdeburg 12 May 2005 Magdeburg, Germany.
  • Gabrys, B., 2005. Multilevel Prediction and Classification Systems. In: Invited Talk, University of Konstanz 11 May 2005 Konstanz, Germany.
  • Gabrys, B., 2005. Multiple Classifier Systems: Issues, Motivations and Challenges. In: Invited Lecture, University of Burgos 14 April 2005 Burgos, Spain.
  • Riedel, S. and Gabrys, B., 2005. Evolving Multilevel Forecast Combination Models - An Experimental Study. In: NiSIS'2005 (Nature-Inspired Smart information Systems) Symposium 4-5 October 2005 Albufeira, Portugal.
  • Gabrys, B., 2005. General Fuzzy Min-Max Neural Networks for Clustering and Classification. In: Invited Lecture, University of Burgos 14 April 2005 Burgos, Spain.
  • Gabrys, B., 2004. Hybrid Intelligent Methods and Smart Adaptive Systems - Keynote Talk. In: EUNITE'2004: European Network of Excellence on Intelligent Technologies for Smart Adaptive Systems Conference 12-14 June 2004 Aachen, Germany.
  • Gabrys, B., 2004. Multiple Classifier Systems: Issues, Motivations and Challenges. In: Invited Talk, Wellington Institute of Technology 10 September 2004 Wellington, New Zealand.
  • Gabrys, B., 2004. Multiple Classifier Systems: Issues, Motivations and Challenges. In: Invited Talk, Electronic Engineering Laboratory 18 February 2004 University of Kent, Canterbury, England.
  • Gabrys, B., 2004. Multiple Classifier Systems: Issues, Motivations and Challenges. In: Invited Talk, Auckland University 17 September 2004 Auckland, New Zealand.
  • Riedel, S. and Gabrys, B., 2004. Hierarchical Multilevel Approaches of Forecast Combination. Springer Berlin Heidelberg, 479-486.
  • Riedel, S. and Gabrys, B., 2003. Adaptive Mechanisms in an Airline Ticket Demand Forecasting System. In: EUNITE'2003 Conference: European Symposium on Intelligent Technologies, Hybrid Systems and their implementation on Smart Adaptive Systems 10-12 July 2003 Oulu, Finland.
  • Gabrys, B., 2003. Multiple Classifier Systems: Issues, Motivations and Challenges. In: Invited talk, Computing Laboratory 2 December 2003 University of Oxford.
  • Gabrys, B., 2003. Multiple Classifier Systems: Issues, Motivations and Challenges. In: Invited Talk, School of Computer Science, University of Birmingham 20 October 2003 Birmingham, England.
  • Gabrys, B., 2003. Multiple Classifier Systems: Issues, Motivations and Challenges. In: Invited Talk, School of Computer Science & Information Technology 26 March 2003 Nottingham University, England.
  • Gabrys, B., 2003. Multiple Classifier Systems: Motivations and Challenges. In: Invited talk, Intelligent Systems Labs of BTexact 31 January 2003 Ipswich, England.
  • Gabrys, B., 2002. Multiple Classifier Systems. In: Department of Enterprise Integration, Cranfield University 8 March 2002 Cranfield, England.
  • Gabrys, B., 2002. Combining Labelled and Unlabelled Data in the Design of Pattern Classification Systems. In: Hybrid Methods for Adaptive Systems (HMAS'2002) Workshop 20 September 2002 Albufeira, Portugal.
  • Gabrys, B., 2002. Combining Neuro-Fuzzy Classifiers for Improved Generalisation and Reliability. In: Burkett, L.D., ed. Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on 12-17 May 2002 Hilton Hawaiian Village Hotel, Honolulu, Hawaii. Piscataway, N. J: IEEE Press, 2410-2415.
  • Ruta, D. and Gabrys, B., 2002. New Measure of Classifier Dependency in Multiple Classifier Systems. Springer Berlin / Heidelberg, 127-136.
  • Petrakieva, L. and Gabrys, B., 2002. Selective Sampling for Combined Learning from Labelled and Unlabelled Data. In: 4th International Conference on Recent Advances in Soft Computing RASC 2002: Proceedings December 2002 Nottingham, England.
  • Ruta, D. and Gabrys, B., 2002. Static Field Approach for Pattern Classification. Berlin: Springer, 232-246.
  • Ruta, D. and Gabrys, B., 2001. Application of the Evolutionary Algorithms for Classifier Selection in Multiple Classifier Systems with Majority Voting. London: Springer Berlin / Heidelberg, 399-408.
  • Ruta, D. and Gabrys, B., 2001. Analysis of the Correlation Between Majority Voting Error and the Diversity Measures in Multiple Classifier Systems. In: Soft Computing and Intelligent Systems for Industry: Proceedings and Scientific Program : Fourth International ICSC Symposium 2001 26-29 June 2001 Paisley, Scotland. ICSC-NAISO Academic Press, 50.
  • Gabrys, B., 2001. Learning Hybrid Neuro-Fuzzy Classifier Models From Data: To Combine or Not to Combine? In: EUNITE'2001 Conference: European Symposium on Intelligent Technologies, Hybrid Systems and their Implementation on Smart Adaptive Systems 13-14 December 2001 Puerto de la Cruz, Tenerife, Spain. Aachen: Elite Foundation.
  • Gabrys, B., 2001. Data and Information Fusion. In: Invited Talk, Lufthansa Systems Berlin GmbH 4 December 2001 Berlin, Germany.
  • Gabrys, B., 2001. Data Editing for Neuro-Fuzzy Classifiers. In: Fourth International ICSC Symposium: Proceedings of the SOCO/ISFI’2001 Conference 26-29 June 2001 Paisley, Scotland. Paisley, Scotland: University of Paisley, 77.
  • Gabrys, B., 2000. Agglomerative Learning for General Fuzzy Min-Max Neural Network. In: IEEE International Workshop on Neural Networks for Signal Processing 11-13 December 2000 Sydney, Australia. IEEE, 692-701.
  • Gabrys, B., 2000. Pattern classification for incomplete data. In: Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on 30 August-1 September 2000 Brighton, England. IEEE, 454-457.
  • Fyfe, C. and Gabrys, B., 1999. E-insensitive Unsupervised Learning. In: International Conference on Neural Networks and Artificial Intelligence (ICNNAI'99) October 1999 Brest, Belarus. Proceedings of International Conference on Neural Networks and Artificial Intelligence ICNNAI'99., 10-18.
  • Gabrys, B. and Bargiela, A., 1998. Simulation of Water Distribution Systems. In: The European Simulation Symposium (ESS'98) 26-28 October 1998 Nottingham, UK. 273-277.
  • Gabrys, B. and Bargiela, A., 1997. Integrated Neural Based System for State Estimation and Confidence Limit Analysis in Water Networks. Society for Computer Simulation, 398-402.
  • Gabrys, B. and Bargiela, A., 1995. Neural Simulation of Water Systems for Efficient State Estimation. In: The European Simulation and Modelling Conference (ESM'95) 5-7 June 1995 Prague, Czech Republic. 775-779.

Reports

Qualifications

  • PhD in Computer Science (Nottingham Trent University, 1998)

Memberships

  • Higher Education Academy, UK (FHEA), Fellow,
  • IEEE Computational Intelligence Society, Member,
  • Institute of Electrical and Electronics Engineers (IEEE) (SMIEEE), Senior Member,
The data on this page was last updated at 04:02 on March 27, 2017.