Dragan Gamberger

Education

Ph.D in Computer sciences, 1986, University of Zagreb

M.Sc in Electrical Engineering, 1978, University of Zagreb

B.Sc in Electrical Engineering, 1974, University of Zagreb

Research Areas

Computing, Technical Sciences, Artificial Intelligence

Specific research interests

applications of intelligent data analysis in scientific research, data clustering, knowledge discovery and representation, knowledge representation by ontologies, machine learning, reasoning based on knowledge, subgroup discovery and descriptive induction

Projects

HRZZ research project:

Machine Learning Algorithms for Insightful Analysis of Complex Data Structures (2014-2017)

Classes

"Knowledge Discovery in Medical Domains" doctoral studies, School of Medicine at University of Zagreb (2003. -)

Featured Publications

Sluban, B., Gamberger, D., Lavrac, N. (2014) Ensemble-based noise detection: Noise ranking and visual performance evaluation. Data Mining and Knowledge Discovery, 28(2):265-303.

Gamberger, D. Smuc, T. (2013) Good Governance Problems and Recent Financial Crises in Some EU Countries.Economics: The Open-Access, Open-Assessment E-Journal, 7:2013-41.

Gamberger, D., Krstacic, G., Jovic, A. (2013) A novel way of integrating rule based knowledge into a web ontology language framework.  In Proc. of Thirteenth EFMI Special Topic Conference "Data and Knowledge for Medical Decision Support", IOS Press, pp. 51-55.

Fuernkranz, J., Gamberger, D., Lavrac, N. (2012). Foundations of Rule Learning. Springer.

Gamberger, D., Lucanin, D. Smuc, T.(2012) Descriptive modeling of systemic banking crises.In Proc. of 15th International Discovery Science Conference, DS 2012, pp.67-80.

Jovic, A., Gamberger, D., Krstacic. G. (2011) Heart failure ontology.Bio-algorithms and med-systems, 7(2):101-110.

Kralj, P., Lavrač, N., Gamberger, D. Krstačić, A. (2009) CSM-SD: Methodology for contrast set mining through subgroup discovery. Journal of Biomedical Informatics, 42/1:113-122.

Lambach, D., Gamberger, D. (2008) Temporal analysis of political instability through descriptive subgroup discovery. Conflict Management and Peace Science, 25:19-32.

Gamberger, D., Lavrač, N., Zelezny, F., Tolar, J. (2004) Induction of comprehensible models for gene expression datasets by subgroup discovery methodology. Journal of Biomedical Informatics, 37/4:269-284.

Baker, J.R., Gamberger, D., Mihelcic, J.R., Sabljić, A. (2004) Evaluation of artificial intelligence based models for chemical biodegradability prediction. Molecules, 9:989-1004.

Gamberger, D., Lavrač, N., Krstačić, G. (2003) Active subgroup mining: A case study in a coronary heart disease risk group detection. Artificial Intelligence in Medicine, 28:27-57.

Gamberger, D. Lavrač, N. (2002) Expert-guided subgroup discovery: Methodology and Application. Journal of Artificial Intelligence Research, 17:501-527

Gamberger, D. (1992) Inversion of integer matrices in residue number system. IEE Proceedings-E, 139:465-468.

Miscellaneous

Authored book:    Fuernkranz, J., Gamberger, D., Lavrac, N. (2012). Foundations of Rule Learning. Springer.