Laboratory for Cognitive Modelling

Laboratory for Cognitive Modeling (LKM) was officially founded in December 2001. LKM carries out research in cognitive modeling, machine learning, neural networks, picture and data mining. Research results concern the modeling of noisy data related to cognitive, medical, biological and other processes. We are developing, testing and applying new approaches and algorithms for modeling from numeric, symbolic and pictorial data. LKM collaborates with psychologists, physicians, biologists, physicists and chemists. A notable aspect of much of this research is its application to problems in image analysis, medical diagnosis, ecological modeling, alternative medicine, studies of consciousness and manifestation of cognitive processes and consciousness through subtle energies.
 

We are located...

in the basement of the faculty main hall (take the stairs near the coffee and wending machines down to the lecture room PR04). The other part of the LKM is located at Jadranska 21, where dr. Robnik and dr. Kukar have their office.

Collaborators

• Prof. Igor Kononenko, PhD, Head
• assoc. prof. Marko Robnik-Šikonja, PhD
• Assist. Prof. Matjaž Kukar, PhD
• Assist. Prof. Zoran Bosnić, PhD
• as. Petar Vračar, MA
• Erik Štrumbelj
• Darko Pevec
• as. Matej Pičulin
• Miha Drole

Selected References

·   M. Bevk, I.Kononenko (2006) Towards symbolic mining of images with association rules: Preliminary results on textures. Intelligent data analysis, pp. 379-393

·   D. Cukjati, M. Robnik-Šikonja, S. Reberšek, I. Kononenko, D. Miklavčič. Prognostic factors, prediction of chronic wound healing and electrical stimulation. Medical & Biological Engineering & Computing, 39, 542–550, 2001.

·   I.Kononenko, N.Lavrač: Prolog Through Examples: A Practical Programming Guide, Sigma Press, 1988.

·   Kononenko. Bayesian Neural Networks, Biological Cybernetics Journal, 61:361–370, 1989.

·   Kononenko, I. Bratko. Information based evaluation criterion for classi­fier’s performance. Machine Learning Journal, 6:67–80, 1991.

·   Kononenko. Inductive and Bayesian learning in medical diagnosis. Applied Artificial Intelligence, 7:317–337, 1993.

·   Kononenko, S.J. Hong. Attribute selection for modeling, Future Generation Computer Systems – ISSN 0167-739X, 13(2–3):181–195, 1997.

·   Kononenko, E. Šimec, M. Robnik. Overcoming the myopia of inductive learning algorithms, Applied Intelligence, 7:39–55, 1997.

·   Kononenko (ed.) Information Society 2000: New Science of Consciousness – Proc. 3rd Int. Conf. on Cognitive science – ISBN 961-6303-27-9 (Ljubljana, 17–19 October 2000), Institut Jožef Stefan, 129 pages.

·   Kononenko: Machine learning for medical diagnosis: History, state of the art and perspective, Invited paper, Artificial Intelligence in Medicine – ISSN 0933-3657, 23(1):89–109, 2001.

·   Kononenko, I. Jerman (eds.). Mind-body studies : proceedings of 6th International Conference on Cognitive Science, Ljubljana, 13-17th October 2003. Ljubljana: Institut “Jožef Stefan”, 190 pages.

·   I.Kononenko: Strojno učenje, 2. popravljena in dopolnjena izdaja, - ISBN 961-6209-52-3, FRI, Ljubljana, 2005.

·   Kononenko, M.Kukar: Machine Learning and Data Mining: Introduction to Principles and Algorithms, Horwood publ., 2006.

·   M. Kukar, N. Bešič, I. Kononenko, M. Aursperg, M. Robnik-Šikonja. Prognosing the survival time of patients with the anaplastic thyroid carcinoma using machine learning, In: N. Lavrač et al. (eds) Intelligent data analysis in medicine and Pharmacology – ISBN 0-7923-8000-2. Kluwer Academic Publ., 1997.

·   M. Kukar, I. Kononenko. Cost-sensitive learning with neural networks, Proc. European conf. on Arificial Intteligence ECAI-98 – ISBN 0-471-98431-0, Brighton, August 1998, pp. 445–449.

·   M. Kukar, I. Kononenko, C. Grošelj, K. Kralj, J. Fettich. Analysing and improving the diagnosis of ischaemic heart disease with machine learn­ing, Artificial Intelligence in Medicine, 16:25–50, 1999.

·   M. Kukar, I. Kononenko, T. Silvester. Machine learning in prognostics of the femoral neck fracture recovery, Artificial intelligence in medicine – ISSN 0933-3657, 8:431–451, 1996.

·   N. Lavrač, I. Kononenko, E. Keravnou, M. Kukar, B. Zupan: Intelligent data analysis for medical diagnosis: using machine learning and temporal abstraction, AI Communications, 11:191–218, 1999.

·   M. Robnik-Šikonja, I. Kononenko. An adaptation of Relief for attribute estimation in regression, Proc. Int. Conf. on Machine Learning ICML-97 – ISBN 1-55860-486-3, Nashville, July 1997, pp. 296–304.

·   M. Robnik-Šikonja, I. Kononenko. Attribute dependencies, understand­ability and split selection in tree based models, Proc. Int. Conf. on Machine Learning ICML-99 – ISBN 1-55860-612-2, Bled, 27–29 June 1999, pp. 344–353.

·   M. Robnik-Šikonja, I. Kononenko. Comprehensible interpretation of relief's estimates. V: BRODLEY, Carla E. (ur.), DANYLUK, Andrea Pohoreckyj (ur.). Machine learning : proceedings of the eighteenth International Conference (ICML 2001) : Williams College, June 28-July 1, 2001, (The Morgan Kaufmann series in machine learning). San Francisco (Calif.): Morgan Kaufmann Publishers, cop. 2001, str. 433-440.

·   M. Robnik-Šikonja, D. Cukjati, I. Kononenko Comprehensible evaluation of prognostic factors and prediction of wound healing. Artificial Intelligence in Medicine, 29: 25-38, 2003.

·   M. Robnik-Šikonja, I. Kononenko. Theoretical and Empirical Analysis of ReliefF and RReliefF, Machine Learning Journal, 53: 23-69, 2003.

·   M. Robnik-Šikonja. Improving Random Forests.  Proc. of  ECML/PKDD’2004, pp. 359-370, Pisa, Itally, 2004.

·   M. Robnik-Šikonja, I. Kononenko. Reliable feature evaluation in classification and regression. In LIU, John X. (ed.). Control and learning in robotic systems. New York: Nova Science Publishers, 2006

·   Sadikov, I. Kononenko, F. Weibel. Analyzing coronas of fruits and leaves. In: K. Korotkov (ed.). Measuring energy fields : state of the science, (GDV bioelectrography series, vol. 1). Fair Lawn: Backbone, 2004, p.143-154

·   L. Šajn, M. Kukar, I. Kononenko,
M. Milčinski. Computerized segmentation of whole-body bone scintigrams and its use in automated diagnostics. Comput.methods programs biomed. [Print ed.], 2005, vol. 80, no. 1, str. [47]-55, ilustr.

·   Trampuž, I. Kononenko, V. Rus. Experiental and biophysical effects of the Art of Living Programme, Int. Journal of Psychology – Abstracts of 27th Int. Congress of Psychology, Stockholm, 23–28 July 2000 – ISSN 0020-7594, 35(3/4)12