Publications

Journal Papers

Zliobaite, I., Budka, M., and Stahl, F., 2014.
Towards cost-sensitive adaptation: When is it worth updating your predictive model?
Neurocomputing, (In press).  [PDF] [BIB]

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), e95648.  [PDF] [BIB]

Lemke, C., Budka, M., and Gabrys, B., 2013.
Metalearning: a survey of trends and technologies.
Artificial Intelligence Review, DOI: 10.1007/s10462-013-9406-y.  [PDF] [BIB]

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, DOI: 10.1007/s13278-013-0101-4.  [PDF] [BIB]

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.  [PDF] [BIB]

Musial, K., Juszczyszyn, K. and Budka, M., 2012.
Triad transition probabilities characterize complex networks.
Awareness Magazine, DOI: 10.2417/3201209.004369.  [PDF] [BIB]

Musial, K., Budka, M. and Juszczyszyn, K., 2012.
Creation and Growth of Online Social Network - How do social networks evolve?
World Wide Web, DOI: 10.1007/s11280-012-0177-1.  [PDF] [BIB]

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.  [PDF] [BIB]

Budka, M. and Gabrys, B., 2011.
Electrostatic field framework for supervised and semi–supervised learning from incomplete data.
Natural Computing, 10 (2), pp. 921-945.  [PDF] [BIB]

Pampanin, D.M., Ravagnan, E., Apeland, S., Aarab, N., Godal, B.F., Westerlund, S., Hjermann, D.O., Eftestol, Budka, M., Gabrys, B., Viarengo, A. and Barsiene, J., 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, 157 (S1), pp. S52.  [BIB]

Budka, M. and Gabrys, B., 2010.
Ridge regression ensemble for toxicity prediction.
Procedia Computer Science, 1 (1), pp. 193-201.  [PDF] [BIB]

Budka, M., Gabrys, B. and Ravagnan, E., 2010.
Robust predictive modelling of water pollution using biomarker data.
Water Research, 44 (10), pp. 3294-3308.  [PDF] [BIB]

Boryczka, U. and Budka, M., 2009.
Finding groups in data: Cluster analysis with ants.
Applied Soft Computing, 9 (1), pp. 61-70.  [PDF] [BIB]


Conference Papers

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.  [PDF] [BIB]

Juszczyszyn, K., Gonczarek, A., Tomczak, J., Musial, K. and Budka, M., 2012.
A Probabilistic Approach to Structural Change Prediction in Evolving Social Networks.
2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012), pp. 1028-1033.  [PDF] [BIB]

Musial, K., Budka, M. and Blysz, W., 2012.
Understanding the Other Side – the Inside Story of the INFER Project.
In: Howlett, R. J. and Jain, L. C., eds. Innovation through Knowledge Transfer 2012, (In press).  [PDF] [BIB]

Juszczyszyn, K., Musial, K. and Budka, M., 2011.
On analysis of complex network dynamics – changes in local topology.
The fifth SNAKDD Workshop 2011 on Social Network Mining and Analysis held in conjunction with SIGKDD conference, pp. 61-70.  [PDF] [BIB]

Juszczyszyn, K., Musial, K. and Budka, M., 2011.
Link Prediction Based on Subgraph Evolution in Dynamic Social Networks.
The 3rd IEEE International Conference on Social Computing (SocialCom 2011), pp. 27-34.  [PDF] [BIB]

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  [PDF] [BIB]

Budka, M., and Gabrys, B., 2010.
Correntropy–based density–preserving data sampling as an alternative to standard cross–validation.
In: IJCNN 2010: International Joint Conference on Neural Networks. IEEE, pp. 1-8.  [PDF] [BIB]


Book Chapters

Budka, M., 2013.
Clustering as an example of optimizing arbitrarily chosen objective functions.
In: Nguyen, N., Trawinski, B., Katarzyniak, R. and Jo, G-S., eds. Advanced Methods for Computational Collective Intelligence. Springer Berlin / Heidelberg, pp. 177-186.  [PDF] [BIB]

Schierz, A. C. and Budka, M., 2011.
High-Performance music information retrieval system for song genre classification.
In: Kryszkiewicz, M., Rybinski, H., Skowron, A. and Ras, Z., eds. Foundations of Intelligent Systems. Heidelberg: Springer, pp. 725-733.  [PDF] [BIB]

Budka, M., and Gabrys, B., 2009.
Electrostatic Field Classifier for Deficient Data.
In: Kurzynski, M. and Wozniak, M., eds. Computer Recognition Systems 3. Heidelberg: Springer, pp. 311-318.  [PDF] [BIB]


Posters

Juszczyszyn, K., Budka, M. and Musial, K., 2011.
Complex Network Model – a New Perspective.
The International School and Conference on Network Science (NetSci2011).  [PDF] [BIB]


Theses

Budka, M., 2010.
Physically inspired methods and development of data-driven predictive systems.
PhD Thesis (PhD). Bournemouth University, UK.  [PDF] [BIB]

Budka, M., 2005.
Ant Colony System for Cluster Analysis
Bachelor’s Thesis (BSc), in Polish. University of Silesia, Katowice, Poland.  [PDF EN] [BIB] [APP]

Budka, M., 2003.
E-banking: threats and safeguards.
Master’s Thesis (MA), in Polish. University of Economics, Katowice, Poland.  [BIB]