Krisztian Buza, PhD
principal investigator of the BioMining project
(the project is closed)
e-mail: buza (at) biointelligence (dot) hu
phone: +49 163 6924 673
LinkedIn profile: https://hu.linkedin.com/in/krisztian-buza-07b10a8
A. Szenkovits, R. Meszlényi, K. Buza, N. Gaskó, R.I. Lung, M. Suciu (2018): Feature Selection with a Genetic Algorithm for Classification of Brain Imaging Data, in U. Stanczyk, B. Zielosko, L.C. Jain: Advances in Feature Selection for Data and Pattern Recognition, Springer
K. Buza, L. Peska (2017): ALADIN: A New Approach for Drug-Target Interaction Prediction, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Springer. [Supplementary experiments - XLSX] [Paper] [Poster] [Slides]
L. Peska, K. Buza, J. Koller (2017): Drug-target interaction prediction: A Bayesian ranking approach [Preprint] [Supplementary material], Computer Methods and Programs in Biomedicine, Vol. 152, pp. 15-21
Regina J. Meszlényi, Krisztian Buza, Zoltán VidnyŠnszky (2017): Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture, Frontiers in Neuroinformatics, Vol. 11
Regina J. Meszlényi, Petra Hermann, Krisztian Buza, Viktor Gál, Zoltán VidnyŠnszky (2017): Resting State fMRI Functional Connectivity Analysis Using Dynamic Time Warping, Frontiers in Neuroscience, Vol. 11
D. Neubrandt, K. Buza (2017): Projection-based Person Identification, Proceedings of the 10th International Conference on Computer Recognition Systems (CORES), Springer. [ipynb] [Ipython Notebook in HTML]
K. Buza, Piroska B. Kis (2017): Towards Privacy-aware Keyboards, Proceedings of the 10th International Conference on Computer Recognition Systems (CORES), Springer. [video]
Regina Meszlényi, Ladislav Peska, Viktor Gal, Zoltán Vidnyánszky, Krisztian Buza (2016): A model for classification based on the functional connectivity pattern dynamics of the brain, The Third European Network Intelligence Conference (ENIC 2016)
Regina Meszlényi, Ladislav Peska, Viktor Gal, Zoltán Vidnyánszky, Krisztian Buza (2016): Classification of fMRI data using Dynamic Time Warping based functional connectivity analysis, 24th European Signal Processing Conference (EUSIPCO)
Rodica Ioana Lung, Mihai Suciu, Regina Meszlényi, Krisztian Buza, Noémi Gaskó (2016): Community structure detection for the functional connectivity networks of the brain, 14th International Conference on Parallel Problem Solving from Nature
K. Buza, N.Á. Varga (2016): ParkinsoNET: Estimation of UPDRS Score using Hubness-aware Feed-Forward Neural Networks, Applied Artificial Intelligence, special issue on Intelligent methods applied to healthcare information systems
K. Buza (2016): Person Identification Based on Keystroke Dynamics: Demo and Open Challenge , 28th International Conference on Advanced Information Systems Engineering (CAiSE'16) Forum
See also: Person Identification Challenge
K. Buza, D. Neubrandt (2016): How You Type Is Who You Are, 11th International Symposium on Applied Computational Intelligence and Informatics, IEEE
K. Buza (2016): Drug-Target Interaction Prediction with Hubness-aware Machine Learning, 11th International Symposium on Applied Computational Intelligence and Informatics, IEEE
K. Buza (2016): Classification of Gene Expression Data: A Hubness-aware Semi-Supervised Approach,[Audio Slides] Computer Methods and Programs in Biomedicine
K. Buza, J. Koller (2016): Classification of Electroencephalograph Data: A Hubness-aware Approach, Acta Polytechnica Hungarica
N. Tomasev, K. Buza, D. Mladenic (2016): Correcting the Hub Occurrence Prediction Bias in Many Dimensions, Computer Science and Information Systems
K. Buza, D. Neubrandt (2016): A New Proposal for Person Identification Based on the Dynamics of Typing: Preliminary Results, Theoretical and Applied Informatics, Vol. 28, No. 1-2
K. Buza, A. Nanopoulos, G. Nagy (2015): Nearest Neighbor Regression in the Presence of Bad Hubs [Preprint] [Audio Slides], Knowledge-Based Systems, Volume 86, pp. 250-260
N. Tomasev, K. Buza (2015): Hubness-aware kNN Classification of High-dimensional Data in Presence of Label Noise, [Preprint] [Audio Slides] Neurocomputing, Volume 160, pp. 157-172
K. Buza, J. Koller, K. Marussy (2015): PROCESS: Projection-Based Classification of Electroencephalograph Signals [paper] [poster], ICAISC, LNCS Vol. 9120, pp. 91-100, Springer.
K. Buza (2015): Semi-supervised Naive Hubness-Bayesian k-Nearest Neighbor for Gene Expression Data, Proceedings of the 9th International Conference on Computer Recognition Systems (CORES), Springer. CORES Award
K. Buza, B. Wilczynski, N. Dojer (2015): RECORD: Reference-Assisted Genome Assembly for Closely Related Genomes, International Journal of Genomics
K. Buza, N. A. Varga (2015): Machine Learning for the Estimation of UPDRS score, VII. Dubrovnik Conference on Cognitive Science (DUCOG), poster
K. Buza (2015): Hubness: An Interesting Property of Nearest Neighbor Graphs and its Impact on Classification, 9th Japanese-Hungarian Symposium on Discrete Mathematics and Its Applications, Invited talk
K. Marussy, L. Peška, K. Buza (2015): Recommendations of Unique Items Based on Bipartite Graphs, 9th Japanese-Hungarian Symposium on Discrete Mathematics and Its Applications
N. Tomasev, K. Buza, K. Marussy, P.B. Kis (2015): Hubness-aware Classification, Instance Selection and Feature Construction: Survey and Extensions to Time-Series, In: U. Stanczyk, L. Jain (eds.), Feature selection for data and pattern recognition, Springer-Verlag
K. Buza, G. Nagy, A. Nanopoulos (2014): Storage-Optimizing Clustering Algorithms for High-Dimensional Tick Data, Expert Systems with Applications, 41, pp. 4148-4157.
K. Marussy, K. Buza (2014): PROGRESS: Projection-Based Gene Expression Classification, Innovations in Medicine Conference (poster)
K. Buza, G. I. Nagy, A. Nanopoulos (2014): Trend analysis and anomaly detection in time series of language usage, VI. Dubrovnik Conference on Cognitive Science (DUCOG), poster
K. Buza, G. I. Nagy, A. Nanopoulos (2014): Three Open Questions related to the Tick Data Decomposition Problem, Summit240 Conference, abstract
Krisztian Buza (2014): Feedback Predicition for Blogs, [paper] [Data used for the experiments in the paper] [presentation slides] The 36th Annual Conference of the German Classification Society on Data Analysis, Machine Learning and Knowledge Discovery August 1-3, 2012, Hildesheim, Germany
K. Buza, K. Marussy (2014): Aspects of Complexity in Data Analysis Tasks - Some Use-Cases, Workshop on Mining Complex Data, 25-27th October, 2014, Kosice, Slovakia
K. Marussy, K. Buza (2013): SUCCESS: A New Approach for Semi-Supervised Classification of Time-Series, ICAISC, LNCS Vol. 7894, pp. 437-447, Springer. The original publication is available at www.springerlink.com.
K. Buza, J. Koller (2013): Speeding up the classification of biomedical signals via instance selection, [poster] [abstract], 5th Dubrovnik Conference on Cognitive Science, Learning & Perception,
K. Buza, I. Galambos (2013): An Application of Link Prediction in Bipartite Graphs: Personalized Blog Feedback Prediction, 8th Japanese-Hungarian Symposium on Discrete Mathematics and Its Applications
F. Bodon, K. Buza (2013): Adatbányászat [Data Mining], Electronic textbook in Hungarian
K. Marussy, K. Buza (2013): Hubness-based indicators for semi-supervised time-series classification, 8th Japanese-Hungarian Symposium on Discrete Mathematics and Its Applications
K. Buza, B. Wilczynski, N. Dojer (2013): A Simple and Effective Technique for Assisted Genome Assembly, 21st Annual International Conference on Intelligent Systems for Molecular Biology & 12th European Conference on Computational Biology, poster
G.I. Nagy, K. Buza (2012): Efficient Storage of Tick Data That Supports Search and Analysis, [paper] [presentation slides], 12th Industrial Conference on Data Mining, Berlin, LNCS Vol. 7377, pp. 38-51, Springer. Nominated for the Best Paper Award.The original publication is available at www.springerlink.com.
K. Buza, A. Nanopoulos, T. Horváth, L. Schmidt-Thieme (2012): GRAMOFON: General Model-selection Framework based on Networks, Neurocomputing, Volume 75, Issue 1, pp. 163-170, Elsevier
G.I. Nagy, K. Buza (2012): Clustering Algorithms for Storage of Tick Data, The 36th Annual Conference of the German Classification Society on Data Analysis, Machine Learning and Knowledge Discovery August 1-3, 2012, Hildesheim, Germany
G.I. Nagy, K. Buza (2012): Partitional Clustering of Tick Data to Reduce Storage Space, IEEE 16th International Conference on Intelligent Engineering Systems
K. Buza, A. Buza, P.B. Kis (2011): A Distributed Genetic Algorithm for Graph-Based Clustering, [paper] [presentation slides] Man-Machine Interactions 2, Advances in Intelligent and Soft Computing, Volume 103/2011, pages 323-331, Springer. The original publication is available at www.springerlink.com.
T. Horváth, A. Eckhardt, K. Buza, P. Vojtás, L. Schmidt-Thieme (2011): Value-transformation for Monotone Prediction by Approximating Fuzzy Membership Functions, [paper] [poster] 12th IEEE International Symposium on Computational Intelligence and Informatics
K. Buza, A. Nanopoulos, L. Schmidt-Thieme (2011): INSIGHT: Efficient and Effective Instance Selection for Time-Series Classification, Proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), LNCS Vol. 6635, pages 149-160, Springer. The original publication is available at www.springerlink.com.
K. Buza, A. Nanopoulos, L. Schmidt-Thieme, J. Koller (2011): Fast Classification of Electrocardiograph Signals via Instance Selection, Proceedings of the First IEEE conference on Healthcare Informatics, Imaging and Systems Biology.
K. Buza (2011): Fusion Methods for Time Series Classification, Peter Lang Verlag.
S. Blohm, K. Buza, P. Cimiano, L. Schmidt-Thieme (2011): Relation Extraction for the Semantic Web with Taxonomic Sequential Patterns, in V. Sugumaran and J.A. Gulla: Applied Semantic Web Technologies, CRC Press, Taylor&Francis Group.
K. Buza, A. Nanopoulos, L. Schmidt-Thieme (2010): Time-Series Classification based on Individualised Error Prediction, 13th IEEE International Conference on Computational Science and Engineering (CSE-2010). Best Paper Award