| Inductive Logic Programming. N Lavrac, S Dzeroski WLP, 146-160, 1994 | 1477 | 1994 |
| The multi-purpose incremental learning system AQ15 and its testing application to three medical domains RS Michalski, I Mozetic, J Hong, N Lavrac Proc. AAAI 1986, 1-041, 1986 | 1239 | 1986 |
| Subgroup discovery with CN2-SD N Lavrac, B Kavsek, P Flach, L Todorovski J. Mach. Learn. Res. 5 (2), 153-188, 2004 | 629* | 2004 |
| Rule evaluation measures: A unifying view N Lavrač, P Flach, B Zupan International conference on inductive logic programming, 174-185, 1999 | 604 | 1999 |
| Foundations of rule learning J Fürnkranz, D Gamberger, N Lavrač Springer Science & Business Media, 2012 | 565 | 2012 |
| Supervised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining. PK Novak, N Lavrač, GI Webb Journal of Machine Learning Research 10 (2), 2009 | 543 | 2009 |
| Selected techniques for data mining in medicine N Lavrač Artificial intelligence in medicine 16 (1), 3-23, 1999 | 538 | 1999 |
| Informal identification of outliers in medical data J Laurikkala, M Juhola, E Kentala, N Lavrac, S Miksch, B Kavsek Fifth international workshop on intelligent data analysis in medicine and …, 2000 | 420 | 2000 |
| Propositionalization approaches to relational data mining S Kramer, N Lavrač, P Flach Relational data mining, 262-291, 2001 | 415 | 2001 |
| A rule based approach to word lemmatization J Plisson, N Lavrac, D Mladenic Proceedings of IS 3, 83-86, 2004 | 373 | 2004 |
| APRIORI-SD: Adapting association rule learning to subgroup discovery B Kavšek, N Lavrač Applied Artificial Intelligence 20 (7), 543-583, 2006 | 339 | 2006 |
| Stream-based active learning for sentiment analysis in the financial domain J Smailović, M Grčar, N Lavrač, M Žnidaršič Information sciences 285, 181-203, 2014 | 331 | 2014 |
| Expert-guided subgroup discovery: Methodology and application D Gamberger, N Lavrac Journal of Artificial Intelligence Research 17, 501-527, 2002 | 293 | 2002 |
| Learning nonrecursive definitions of relations with LINUS N Lavrač, S Džeroski, M Grobelnik Machine Learning—EWSL-91: European Working Session on Learning Porto …, 1991 | 289 | 1991 |
| KARDIO: a study in deep and qualitative knowledge for expert systems I Bratko, I Mozetič, N Lavrač MIT press, 1990 | 288 | 1990 |
| Experiments with noise filtering in a medical domain D Gamberger, N Lavrac, C Groselj ICML 99, 143-151, 1999 | 259 | 1999 |
| The AQ15 inductive learning system: an overview and experiments RS Michalski Technical report, MLI-86-6, Machine Learning and Inference Laboratory …, 1986 | 252 | 1986 |
| Predictive sentiment analysis of tweets: A stock market application J Smailović, M Grčar, N Lavrač, M Žnidaršič Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured …, 2013 | 193 | 2013 |
| Comparative evaluation of approaches to propositionalization MA Krogel, S Rawles, F Železný, PA Flach, N Lavrač, S Wrobel International Conference on Inductive Logic Programming, 197-214, 2003 | 193 | 2003 |
| Noise detection and elimination in data preprocessing: experiments in medical domains D Gamberger, N Lavrac, S Dzeroski Applied artificial intelligence 14 (2), 205-223, 2000 | 161 | 2000 |
Senja Pollakresearcher, Jožef Stefan Institute, Coordinator of EMBEDDIA (H2020)Verified email at ijs.si