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Nada Lavrac: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Ryszard S. Michalski, Igor Mozetic, Jiarong Hong, Nada Lavrac
    The Multi-Purpose Incremental Learning System AQ15 and Its Testing Application to Three Medical Domains. [Citation Graph (4, 0)][DBLP]
    AAAI, 1986, pp:1041-1047 [Conf]
  2. Saso Dzeroski, Nada Lavrac
    Inductive Learning in Deductive Databases. [Citation Graph (2, 15)][DBLP]
    IEEE Trans. Knowl. Data Eng., 1993, v:5, n:6, pp:939-949 [Journal]
  3. Nada Lavrac, Saso Dzeroski
    Background Knowledge and Declarative Bias in Inductive Concept Learning. [Citation Graph (1, 0)][DBLP]
    AII, 1992, pp:51-71 [Conf]
  4. Nada Lavrac, Saso Dzeroski, Marko Grobelnik
    Learning Nonrecursive Definitions of Relations with LINUS. [Citation Graph (1, 0)][DBLP]
    EWSL, 1991, pp:265-281 [Conf]
  5. Saso Dzeroski, Nada Lavrac
    Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL. [Citation Graph (1, 0)][DBLP]
    ML, 1991, pp:399-402 [Conf]
  6. Dragan Gamberger, Nada Lavrac, Ciril Groselj
    Diagnostic Rules of Increased Reliability for Critical Medical Applications. [Citation Graph (0, 0)][DBLP]
    AIMDM, 1999, pp:361-365 [Conf]
  7. Nada Lavrac
    Machine Learning for Data Mining in Medicine. [Citation Graph (0, 0)][DBLP]
    AIMDM, 1999, pp:47-64 [Conf]
  8. Dragan Gamberger, Nada Lavrac
    Analysis of Gene Expression Data by the Logic Minimization Approach. [Citation Graph (0, 0)][DBLP]
    AIME, 2003, pp:244-248 [Conf]
  9. Nada Lavrac, Marko Bohanec, Aleksander Pur, Bojan Cestnik, Mitja Jermol, Tanja Urbancic, Marko Debeljak, Branko Kavsek, Tadeja Kopac
    Resource Modeling and Analysis of Regional Public Health Care Data by Means of Knowledge Technologies. [Citation Graph (0, 0)][DBLP]
    AIME, 2005, pp:414-418 [Conf]
  10. Igor Zelic, Igor Kononenko, Nada Lavrac, Vanja Vuga
    Machine Learning Applied to Diagnosis of Sport Injuries. [Citation Graph (0, 0)][DBLP]
    AIME, 1997, pp:138-141 [Conf]
  11. Dragan Gamberger, Nada Lavrac, Saso Dzeroski
    Noise Elimination in Inductive Concept Learning: A Case Study in Medical Diagnosois. [Citation Graph (0, 0)][DBLP]
    ALT, 1996, pp:199-212 [Conf]
  12. Nada Lavrac, Dragan Gamberger, Peter D. Turney
    Cost-Sensitive Feature Reduction Applied to a Hybrid Genetic Algorithm. [Citation Graph (0, 0)][DBLP]
    ALT, 1996, pp:127-134 [Conf]
  13. Peter A. Flach, Nada Lavrac
    Learning in Clausal Logic: A Perspective on Inductive Logic Programming. [Citation Graph (0, 0)][DBLP]
    Computational Logic: Logic Programming and Beyond, 2002, pp:437-471 [Conf]
  14. Iztok A. Pilih, Dunja Mladenic, Nada Lavrac, Tine S. Prevec
    Using machine learning for outcome prediction of patients with severe head injury. [Citation Graph (0, 0)][DBLP]
    CBMS, 1997, pp:200-204 [Conf]
  15. Igor Zelic, Igor Kononenko, Nada Lavrac, Vanja Vuga
    Diagnosis of sport injuries with machine learning: explanation of induced decisions. [Citation Graph (0, 0)][DBLP]
    CBMS, 1997, pp:195-199 [Conf]
  16. Nada Lavrac, Dragan Gamberger
    Relevancy in Constraint-Based Subgroup Discovery. [Citation Graph (0, 0)][DBLP]
    Constraint-Based Mining and Inductive Databases, 2004, pp:243-266 [Conf]
  17. Igor Trajkovski, Filip Zelezný, Jakub Tolar, Nada Lavrac
    Relational Subgroup Discovery for Descriptive Analysis of Microarray Data. [Citation Graph (0, 0)][DBLP]
    CompLife, 2006, pp:86-96 [Conf]
  18. Nada Lavrac, Filip Zelezný, Saso Dzeroski
    Local Patterns: Theory and Practice of Constraint-Based Relational Subgroup Discovery. [Citation Graph (0, 0)][DBLP]
    Local Pattern Detection, 2004, pp:71-88 [Conf]
  19. Dragan Gamberger, Nada Lavrac
    Avoiding Data Overfitting in Scientific Discovery: Experiments in Functional Genomics. [Citation Graph (0, 0)][DBLP]
    ECAI, 2004, pp:470-474 [Conf]
  20. Matevz Kovacic, Nada Lavrac, Marko Grobelnik, Darko Zupanic, Dunja Mladenic
    Stochastic Search in Inductive Logic Programming. [Citation Graph (0, 0)][DBLP]
    ECAI, 1992, pp:444-445 [Conf]
  21. Dragan Gamberger, Nada Lavrac
    Conditions for Occam's Razor Applicability and Noise Elimination. [Citation Graph (0, 0)][DBLP]
    ECML, 1997, pp:108-123 [Conf]
  22. Matjaz Gams, Nada Lavrac
    Review of Five Empirical Learning Systems Within a Proposed Schemata. [Citation Graph (0, 0)][DBLP]
    EWSL, 1987, pp:46-66 [Conf]
  23. Branko Kavsek, Nada Lavrac, Anuska Ferligoj
    Consensus Decision Trees: Using Consensus Hierarchical Clustering for Data Relabelling and Reduction. [Citation Graph (0, 0)][DBLP]
    ECML, 2001, pp:251-262 [Conf]
  24. Viktor Jovanoski, Nada Lavrac
    Classification Rule Learning with APRIORI-C. [Citation Graph (0, 0)][DBLP]
    EPIA, 2001, pp:44-51 [Conf]
  25. Nada Lavrac
    Challenges for Inductive Logic Programming. [Citation Graph (0, 0)][DBLP]
    EPIA, 1999, pp:16-33 [Conf]
  26. Nada Lavrac, Peter A. Flach, Branko Kavsek, Ljupco Todorovski
    Adapting classification rule induction to subgroup discovery. [Citation Graph (0, 0)][DBLP]
    ICDM, 2002, pp:266-273 [Conf]
  27. Nada Lavrac
    Inductive Logic Programming for Relational Knowledge Discovery. [Citation Graph (0, 0)][DBLP]
    IJCSLP, 1998, pp:7-24 [Conf]
  28. Dragan Gamberger, Nada Lavrac
    Descriptive Induction through Subgroup Discovery: A Case Study in a Medical Domain. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:163-170 [Conf]
  29. Dragan Gamberger, Nada Lavrac, Ciril Groselj
    Experiments with Noise Filtering in a Medical Domain. [Citation Graph (0, 0)][DBLP]
    ICML, 1999, pp:143-151 [Conf]
  30. Branko Kavsek, Nada Lavrac, Viktor Jovanoski
    APRIORI-SD: Adapting Association Rule Learning to Subgroup Discovery. [Citation Graph (0, 0)][DBLP]
    IDA, 2003, pp:230-241 [Conf]
  31. Nada Lavrac, Peter Ljubic, Mitja Jermol, Gregor Papa
    A Decision Support Approach to Modeling Trust in Networked Organizations. [Citation Graph (0, 0)][DBLP]
    IEA/AIE, 2005, pp:746-748 [Conf]
  32. Aleksander Pur, Marko Bohanec, Bojan Cestnik, Nada Lavrac, Marko Debeljak, Tadeja Kopac
    Data Mining for Decision Support: An Application in Public Health Care. [Citation Graph (0, 0)][DBLP]
    IEA/AIE, 2005, pp:459-469 [Conf]
  33. Mitja Jermol, Nada Lavrac, Tanja Urbancic, Tadeja Kopac
    Supporting a Public Health Care Virtual Organization by Knowledge Technologies. [Citation Graph (0, 0)][DBLP]
    Virtual Enterprises and Collaborative Networks, 2004, pp:567-576 [Conf]
  34. Nada Lavrac
    Virtual Enterprise for Data Mining and Decision Support. [Citation Graph (0, 0)][DBLP]
    PRO-VE, 2002, pp:- [Conf]
  35. Luc De Raedt, Nada Lavrac, Saso Dzeroski
    Multiple Predicate Learning. [Citation Graph (0, 0)][DBLP]
    IJCAI, 1993, pp:1037-1043 [Conf]
  36. Mark-A. Krogel, Simon Rawles, Filip Zelezný, Peter A. Flach, Nada Lavrac, Stefan Wrobel
    Comparative Evaluation of Approaches to Propositionalization. [Citation Graph (0, 0)][DBLP]
    ILP, 2003, pp:197-214 [Conf]
  37. Nada Lavrac, Peter A. Flach, Blaz Zupan
    Rule Evaluation Measures: A Unifying View. [Citation Graph (0, 0)][DBLP]
    ILP, 1999, pp:174-185 [Conf]
  38. Dragan Gamberger, Nada Lavrac
    Noise Detection and Elimination Applied to Noise Handling in a KRK Chess Endgame. [Citation Graph (0, 0)][DBLP]
    Inductive Logic Programming Workshop, 1996, pp:72-88 [Conf]
  39. Nada Lavrac, Filip Zelezný, Peter A. Flach
    RSD: Relational Subgroup Discovery through First-Order Feature Construction. [Citation Graph (0, 0)][DBLP]
    ILP, 2002, pp:149-165 [Conf]
  40. Dragan Gamberger, Nada Lavrac, Goran Krstacic, Tomislav Smuc
    Inconsistency Tests for Patient Records in a Coronary Heart Disease Database. [Citation Graph (0, 0)][DBLP]
    ISMDA, 2000, pp:183-189 [Conf]
  41. Luc De Raedt, Nada Lavrac
    The Many Faces of Inductive Logic Programming. [Citation Graph (0, 0)][DBLP]
    ISMIS, 1993, pp:435-449 [Conf]
  42. Nada Lavrac
    Subgroup Discovery Techniques and Applications. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2005, pp:2-14 [Conf]
  43. Dragan Gamberger, Nada Lavrac
    Confirmation Rule Sets. [Citation Graph (0, 0)][DBLP]
    PKDD, 2000, pp:34-43 [Conf]
  44. Dragan Gamberger, Nada Lavrac
    Generating Actionable Knowledge by Expert-Guided Subgroup Discovery. [Citation Graph (0, 0)][DBLP]
    PKDD, 2002, pp:163-174 [Conf]
  45. Gemma C. Garriga, Petra Kralj, Nada Lavrac
    Closed Sets for Labeled Data. [Citation Graph (0, 0)][DBLP]
    PKDD, 2006, pp:163-174 [Conf]
  46. Ljupco Todorovski, Peter A. Flach, Nada Lavrac
    Predictive Performance of Weghted Relative Accuracy. [Citation Graph (0, 0)][DBLP]
    PKDD, 2000, pp:255-264 [Conf]
  47. Branko Kavsek, Nada Lavrac, Ljupco Todorovski
    ROC Analysis of Example Weighting in Subgroup Discovery. [Citation Graph (0, 0)][DBLP]
    ROCAI, 2004, pp:55-60 [Conf]
  48. Nada Lavrac, Saso Dzeroski, Vladimir Pirnat, Viljem Krizman
    Learning Rules for Early Diagnosis of Rheumatic Diseases. [Citation Graph (0, 0)][DBLP]
    SCAI, 1991, pp:138-149 [Conf]
  49. Nada Lavrac
    Inductive Logic Programming. [Citation Graph (0, 0)][DBLP]
    WLP, 1994, pp:146-160 [Conf]
  50. Dragan Gamberger, Nada Lavrac, Saso Dzeroski
    Noise Detection and Elimination in data Proprocessing: Experiments in Medical Domains. [Citation Graph (0, 0)][DBLP]
    Applied Artificial Intelligence, 2000, v:14, n:2, pp:205-223 [Journal]
  51. Branko Kavsek, Nada Lavrac
    APRIORI-SD: Adapting Association Rule Learning to Subgroup Discovery. [Citation Graph (0, 0)][DBLP]
    Applied Artificial Intelligence, 2006, v:20, n:7, pp:543-583 [Journal]
  52. Nada Lavrac, Saso Dzeroski, Vladimir Pirnat, Viljem Krizman
    The utility of background knowledge in learning medical diagnostic rules. [Citation Graph (0, 0)][DBLP]
    Applied Artificial Intelligence, 1993, v:7, n:3, pp:273-293 [Journal]
  53. Nada Lavrac, Luc De Raedt
    Inductive Logic Programming: A Survey of European Research. [Citation Graph (0, 0)][DBLP]
    AI Commun., 1995, v:8, n:1, pp:3-19 [Journal]
  54. Nada Lavrac, Irene Weber, Darko Zupanic, Dimitar Kazakov, Olga Stepánková, Saso Dzeroski
    ILPNET Repositories on WWW: Inductive Logic Programming Systems, Datasets and Bibliography. [Citation Graph (0, 0)][DBLP]
    AI Commun., 1996, v:9, n:4, pp:157-206 [Journal]
  55. Nada Lavrac, Blaz Zupan, Igor Kononenko, Matjaz Kukar, Elpida T. Keravnou
    Intelligent Data Analysis for Medical Diagnosis: Using Machine Learning and Temporal Abstraction. [Citation Graph (0, 0)][DBLP]
    AI Commun., 1998, v:11, n:3-4, pp:191-218 [Journal]
  56. Dragan Gamberger, Nada Lavrac
    Active subgroup mining: a case study in coronary heart disease risk group detection. [Citation Graph (0, 0)][DBLP]
    Artificial Intelligence in Medicine, 2003, v:28, n:1, pp:27-57 [Journal]
  57. Elpida T. Keravnou, Nada Lavrac
    AIM portraits: tracing the evolution of artificial intelligence in medicine and predicting its future in the new millennium. [Citation Graph (0, 0)][DBLP]
    Artificial Intelligence in Medicine, 2001, v:23, n:1, pp:1-4 [Journal]
  58. Nada Lavrac
    Selected techniques for data mining in medicine. [Citation Graph (0, 0)][DBLP]
    Artificial Intelligence in Medicine, 1999, v:16, n:1, pp:3-23 [Journal]
  59. Saso Dzeroski, Nada Lavrac
    Editorial. [Citation Graph (0, 0)][DBLP]
    Data Min. Knowl. Discov., 1999, v:3, n:1, pp:5-6 [Journal]
  60. Nada Lavrac, Dragan Gamberger, Peter D. Turney
    A Relevancy Filter for Constructive Induction. [Citation Graph (0, 0)][DBLP]
    IEEE Intelligent Systems, 1998, v:13, n:2, pp:50-56 [Journal]
  61. Dragan Gamberger, Nada Lavrac
    Expert-Guided Subgroup Discovery: Methodology and Application. [Citation Graph (0, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 2002, v:17, n:, pp:501-527 [Journal]
  62. Dragan Gamberger, Nada Lavrac, Filip Zelezný, Jakub Tolar
    Induction of comprehensible models for gene expression datasets by subgroup discovery methodology. [Citation Graph (0, 0)][DBLP]
    Journal of Biomedical Informatics, 2004, v:37, n:4, pp:269-284 [Journal]
  63. Darko Zupanic, Milan Hodoscek, Nada Lavrac, Igor Mozetic
    Global Energy Minimization of Small Molecules Combining Constraint Logic Programming and Molecular Mechanics. [Citation Graph (0, 0)][DBLP]
    Journal of Chemical Information and Computer Sciences, 1997, v:37, n:6, pp:966-970 [Journal]
  64. Dragan Gamberger, Nada Lavrac, Goran Krstacic
    Confirmation rule induction and its applications to coronary heart disease diagnosis and risk group discovery. [Citation Graph (0, 0)][DBLP]
    Journal of Intelligent and Fuzzy Systems, 2002, v:12, n:1, pp:35-48 [Journal]
  65. Mitja Jermol, Nada Lavrac, Tanja Urbancic
    Managing business intelligence in a virtual enterprise: A case study and knowledge management lessons learned. [Citation Graph (0, 0)][DBLP]
    Journal of Intelligent and Fuzzy Systems, 2003, v:14, n:3, pp:121-136 [Journal]
  66. Nada Lavrac, Dragan Gamberger, Viktor Jovanoski
    A Study of Relevance for Learning in Deductive Databases. [Citation Graph (0, 0)][DBLP]
    J. Log. Program., 1999, v:40, n:2-3, pp:215-249 [Journal]
  67. Nada Lavrac, Branko Kavsek, Peter A. Flach, Ljupco Todorovski
    Subgroup Discovery with CN2-SD. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2004, v:5, n:, pp:153-188 [Journal]
  68. Nada Lavrac, Stefan Wrobel
    Induktive Logikprogrammierung - Grundlagen und Techniken. [Citation Graph (0, 0)][DBLP]
    KI, 1996, v:10, n:3, pp:46-54 [Journal]
  69. Nada Lavrac, Bojan Cestnik, Dragan Gamberger, Peter A. Flach
    Decision Support Through Subgroup Discovery: Three Case Studies and the Lessons Learned. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2004, v:57, n:1-2, pp:115-143 [Journal]
  70. Nada Lavrac, Saso Dzeroski
    A Reply to Pazzani's Book Review of ``Inductive Logic Programming: Techniques and Applications''. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1996, v:23, n:1, pp:109-111 [Journal]
  71. Nada Lavrac, Hiroshi Motoda, Tom Fawcett
    Editorial: Data Mining Lessons Learned. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2004, v:57, n:1-2, pp:5-11 [Journal]
  72. Nada Lavrac, Hiroshi Motoda, Tom Fawcett, Robert Holte, Pat Langley, Pieter W. Adriaans
    Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2004, v:57, n:1-2, pp:13-34 [Journal]
  73. Filip Zelezný, Nada Lavrac
    Propositionalization-based relational subgroup discovery with RSD. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2006, v:62, n:1-2, pp:33-63 [Journal]
  74. Nada Lavrac, Saso Dzeroski, Masayuki Numao
    Inductive Logic Programming for Relational Knowledge Discovery. [Citation Graph (0, 0)][DBLP]
    New Generation Comput., 1999, v:17, n:1, pp:3-23 [Journal]
  75. Nada Lavrac, Peter A. Flach
    An extended transformation approach to inductive logic programming. [Citation Graph (0, 0)][DBLP]
    ACM Trans. Comput. Log., 2001, v:2, n:4, pp:458-494 [Journal]
  76. Dragan Gamberger, Nada Lavrac
    Supporting Factors in Descriptive Analysis of Brain Ischaemia. [Citation Graph (0, 0)][DBLP]
    AIME, 2007, pp:155-159 [Conf]
  77. Petra Kralj, Nada Lavrac, Dragan Gamberger, Antonija Krstacic
    Contrast Set Mining for Distinguishing Between Similar Diseases. [Citation Graph (0, 0)][DBLP]
    AIME, 2007, pp:109-118 [Conf]
  78. Aleksander Pur, Marko Bohanec, Nada Lavrac, Bojan Cestnik, Marko Debeljak, Anton Gradisek
    Monitoring Human Resources of a Public Health-Care System Through Intelligent Data Analysis and Visualization. [Citation Graph (0, 0)][DBLP]
    AIME, 2007, pp:175-179 [Conf]
  79. Igor Trajkovski, Nada Lavrac
    Interpreting Gene Expression Data by Searching for Enriched Gene Sets. [Citation Graph (0, 0)][DBLP]
    AIME, 2007, pp:144-148 [Conf]
  80. Damjan Demsar, Igor Mozetic, Nada Lavrac
    Collaboration Opportunity Finder. [Citation Graph (0, 0)][DBLP]
    Virtual Enterprises and Collaborative Networks, 2007, pp:179-186 [Conf]
  81. Monika Záková, Filip Zelezný, Javier A. Garcia-Sedano, Cyril Masia Tissot, Nada Lavrac, Petr Kremen, Javier Molina
    Relational Data Mining Applied to Virtual Engineering of Product Designs. [Citation Graph (0, 0)][DBLP]
    ILP, 2006, pp:439-453 [Conf]
  82. Petra Kralj, Nada Lavrac, Dragan Gamberger, Antonija Krstacic
    Contrast Set Mining Through Subgroup Discovery Applied to Brain Ischaemina Data. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2007, pp:579-586 [Conf]
  83. Igor Trajkovski, Nada Lavrac
    Efficient Generation of Biologically Relevant Enriched Gene Sets. [Citation Graph (0, 0)][DBLP]
    ISBRA, 2007, pp:248-259 [Conf]
  84. Nada Lavrac, Marko Bohanec, Aleksander Pur, Bojan Cestnik, Marko Debeljak, Andrej Kobler
    Data mining and visualization for decision support and modeling of public health-care resources. [Citation Graph (0, 0)][DBLP]
    Journal of Biomedical Informatics, 2007, v:40, n:4, pp:438-447 [Journal]
  85. Nada Lavrac, Peter Ljubic, Tanja Urbani, Gregor Papa, Mitja Jermol, S. Bollhalter
    Trust Modeling for Networked Organizations Using Reputation and Collaboration Estimates. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Systems, Man, and Cybernetics, Part C, 2007, v:37, n:3, pp:429-439 [Journal]

  86. Advances in Class Noise Detection. [Citation Graph (, )][DBLP]


  87. On the Design of Knowledge Discovery Services Design Patterns and Their Application in a Use Case Implementation. [Citation Graph (, )][DBLP]


  88. Handling Unknown and Imprecise Attribute Values in Propositional Rule Learning: A Feature-Based Approach. [Citation Graph (, )][DBLP]


  89. Advancing Topic Ontology Learning through Term Extraction. [Citation Graph (, )][DBLP]


  90. Semi-supervised Constrained Clustering: An Expert-Guided Data Analysis Methodology. [Citation Graph (, )][DBLP]


  91. Relational Descriptive Analysis of Gene Expression Data. [Citation Graph (, )][DBLP]


  92. SolEuNet: Selected Data Mining Techniques and Applications. [Citation Graph (, )][DBLP]


  93. Ripple Down Rule learning for automated word lemmatisation. [Citation Graph (, )][DBLP]


  94. Clinical data analysis based on iterative subgroup discovery: experiments in brain ischaemia data analysis. [Citation Graph (, )][DBLP]


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