Some papers ordered by date

• Marko Robnik-Sikonja, Kris Brijs, Koen Vanhoof: Ordinal Evaluation: A New Perspective on Country Images. In P. Perner (ed.): Advances in Data Mining, Proceedings of Industrial Conference on Data Mining, ICDM 2009. LNAI 5633, Springer, 2009 PDF
• Marko Robnik-Sikonja, Aristidis Likas, Constantinos Constantinopoulos, Igor Kononenko: An Efficient Method for Explaining the Decisions of the Probabilistic RBF Classification Network. Technical Report, University of Ljubljana, Faculty of Computer and Information Science, Slovenia, and University of Ioannina, Department of Computer Science, 2009  PDF
• Marko Robnik-Sikonja, Igor Kononenko: Explaining Classifications For Individual Instances. IEEE Transactions on Knowledge and Data Engineering, 20: 589-600, 2008 PDF
• Petr Savicky, Marko Robnik-Sikonja: Learning Random Numbers: A Matlab Anomaly. Applied Artificial Intelligence, 22: 254-265, 2008  PDF
• Marko Robnik-Sikonja, Koen Vanhoof: Evaluation of ordinal attributes at value level. Knowledge Discovery and Data Mining, 14:225-243, 2007 PDF
• Marko Robnik-Sikonja: Improving Random Forests. In J.-F. Boulicaut et al.(Eds): ECML 2004, LNAI 3210, Springer, Berlin, 2004, pp. 359-370  PDF
• Marko Robnik-Sikonja, Igor Kononenko: Theoretical and Empirical Analysis of ReliefF and RReliefF. Machine Learning Journal, 53:23-69, 2003 PDF
• Marko Robnik-Sikonja, David Cukjati, Igor Kononenko: Comprehensible evaluation of prognostic factors and prediction of wound healing. Artificial Intelligence in Medicine, 29: 25-38, 2003  PDF
• Marko Robnik-Sikonja: Experiments with Cost-sensitive Feature Evaluation. In Lavrac et al.(eds): Machine Learning, Proceedings of ECML 2003, Springer, Berlin, 2003, pp. 325-336  PDF
• Marko Robnik-Sikonja, Igor Kononenko: Comprehensible Interpretation of Relief's Estimates. In C. Brodley, A. Danyluk (eds): Machine Learning, Proceedings of 18th International Conference on Machine Learning ICML'2001, Williamstown, MA, 2001 postscript
• Marko Robnik -Sikonja: Features of the heuristic function Relief and its use in machine learning. Ph.D. Thesis, University of Ljubljana,  2001 (in Slovene) postscript
• Marko Robnik - Sikonja, David Cukjati, Igor Kononenko: Evaluation of prognostic factors and prediction of chronic wound healing rate by machine learning tools  In  Proceedings of Artificial Intelligence in Medicine Europe, AIME 2001, Cascais, Portugal,  2001,  postscript
• David Cukjati, Marko Robnik-Šikonja, Stanislav Reberšek, Igor Kononenko, Damijan Miklavčič: Prognostic factors in the prediction of chronic wound healing by electrical stimulation. Medical & Biological Engineering & Computing, 39:542-550, 2001 PDF 252 KB
• Marko Robnik- Sikonja, Igor Kononenko: Attribute dependencies, understandability and split selection in tree based models. In I. Bratko, S. Dzeroski (eds): Machine Learning, Proceedings of 16th International Conference on Machine Learning ICML'99, Bled, Slovenia, 1999 postscript
• Marko Robnik-Sikonja, Igor Kononenko: Comprehensiveness of tree based models: attribute dependencies and split selection. In M. Grobelnik, D. Mladenic (eds): Analysis, Warehousing and Mining the Data AWAMIDA'99 , Ljubljana, Slovenia, 1999 postscript
• Marko Robnik-Sikonja, Igor Kononenko: Pruning regression trees with MDL. In H. Prade (ed.): ECAI'98, Proceedings of 13th European Conference on Artificial Intelligence, , Brighton, UK, Wiley , 1998 postscript
• Marko Robnik-Sikonja: Speeding up Relief algorithms with k-d trees (corrected version). Proceedings of ERK'98 , Portoroz, Slovenia, 1998. postscript
• Igor Kononenko, Edvard Simec, Marko Robnik-Sikonja: Overcoming the myopia of inductive learning algorithms with ReliefF. Applied Intelligence, 7:39-55 1997, postscript
• Marko Robnik-Sikonja, Igor Kononenko: An adaptation of Relief for attribute estimation in regression. In D.Fisher (ed.): Machine Learning, Proceedings of 14th International Conference on Machine Learning ICML'97, Nashville, TN, 1997 postscript
• Marko Robnik-Sikonja: CORE - a system that predicts continuous variables. Proceedings of ERK'97 , Portoroz, Slovenia, 1997. postscript
• Marko Robnik-Sikonja: The developement of the heuristics for guiding the learning of the regression trees. MSc thesis, University of Ljubljana, Faculty of Computer and Information Science, 1997 (in Slovene). (Razvoj hevristik za usmerjanje ucenja regresijskih dreves.) postscript
• Igor Kononenko, Marko Robnik-Sikonja, Uros Pompe: ReliefF for estimation and discretization of attributes in classification, regression and ILP problems. In A. Ramsay (ed.): Artificial Intelligence: Methodology, Systems, Applications: Proceddings of AIMSA'96, pp.31-40, IOS Press, 1996 postscript
• Marko Robnik-Sikonja: Effective use of memory in linear space best first search. Technical report, January 96, postscript
• Marko Robnik-Sikonja, Igor Kononenko: Non-myopic attribute estimation in regression. Technical report, January 1996, postscript
• Marko Robnik-Sikonja, Igor Kononenko: Discretization of continuous attributes using ReliefF. Proceedings of ERK'95 , Portoroz, Slovenia, 1995. postscript
• Marko Robnik: Konstruktivna indukcija v strojnem ucenju. Elektrotehniski vestnik, 1995 (in Slovene), postscript
• Marko Robnik: Konstruktivna indukcija z odločitvenimi drevesi. Diplomsko delo, Fakulteta za elektrotehniko in raèunalništvo, Ljubljana, 1993 (in Slovene), postscript

The majority of the algorithms from the papers are implemented in the learning system CORE which is freely available. Most of other sources in R, python, and Matlab are available upon email request.