{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T18:48:08Z","timestamp":1784054888831,"version":"3.55.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2009,12,9]],"date-time":"2009-12-09T00:00:00Z","timestamp":1260316800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2010,3]]},"DOI":"10.1007\/s10994-009-5162-2","type":"journal-article","created":{"date-parts":[[2009,12,8]],"date-time":"2009-12-08T20:22:10Z","timestamp":1260303730000},"page":"343-379","source":"Crossref","is-referenced-by-count":47,"title":["On the quest for optimal rule learning heuristics"],"prefix":"10.1007","volume":"78","author":[{"given":"Frederik","family":"Janssen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"F\u00fcrnkranz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2009,12,9]]},"reference":[{"issue":"6","key":"5162_CR1","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","volume":"19","author":"H. Akaike","year":"1974","unstructured":"Akaike,\u00a0H. (1974). A new look at the statistical model selection. IEEE Transactions on Automatic Control, 19(6), 716\u2013723.","journal-title":"IEEE Transactions on Automatic Control"},{"key":"5162_CR2","unstructured":"Asuncion,\u00a0A., & Newman,\u00a0D. (2007). UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml\/ ."},{"key":"5162_CR3","doi-asserted-by":"crossref","unstructured":"Bayardo,\u00a0R.\u00a0Jr., & Agrawal,\u00a0R. (1999). Mining the most interesting rules. In Proceedings of the 5th ACM SIGKDD international conference on knowledge discovery and data mining (KDD-97) (pp.\u00a0145\u2013154).","DOI":"10.1145\/312129.312219"},{"key":"5162_CR4","doi-asserted-by":"crossref","unstructured":"Brin,\u00a0S., Motwani,\u00a0R., & Silverstein,\u00a0C. (1997). Beyond market baskets: generalizing association rules to correlations. In Proceedings of the ACM SIGMOD international conference on management of data (pp.\u00a0265\u2013276).","DOI":"10.1145\/253260.253327"},{"key":"5162_CR5","first-page":"75","volume":"8","author":"W. Buntine","year":"1992","unstructured":"Buntine,\u00a0W., & Niblett,\u00a0T. (1992). A further comparison of splitting rules for decision-tree induction. Machine Learning, 8, 75\u201385.","journal-title":"Machine Learning"},{"key":"5162_CR6","unstructured":"Burges,\u00a0S. (2006). Meta-Lernen einer Evaluierungs-Funktion f\u00fcr einen Regel-Lerner. Master\u2019s thesis, TU Darmstadt, December 2006 (in German) (English title: Meta-learning of an evaluation function for a rule learner)."},{"key":"5162_CR7","first-page":"147","volume-title":"Proceedings of the 9th European conference on artificial intelligence","author":"B. Cestnik","year":"1990","unstructured":"Cestnik,\u00a0B. (1990). Estimating probabilities: a\u00a0crucial task in machine learning. In L.\u00a0Aiello (Ed.), Proceedings of the 9th European conference on artificial intelligence (pp.\u00a0147\u2013150). ECAI-90, Stockholm, Sweden, 1990. London: Pitman."},{"key":"5162_CR8","first-page":"151","volume-title":"Proceedings of the 5th European working session on learning","author":"P. Clark","year":"1991","unstructured":"Clark,\u00a0P., & Boswell,\u00a0R. (1991). Rule induction with CN2: Some recent improvements. In Proceedings of the 5th European working session on learning (pp.\u00a0151\u2013163). EWSL-91, Porto, Portugal, 1991. Berlin: Springer."},{"issue":"4","key":"5162_CR9","first-page":"261","volume":"3","author":"P. Clark","year":"1989","unstructured":"Clark,\u00a0P., & Niblett,\u00a0T. (1989). The CN2 induction algorithm. Machine Learning, 3(4), 261\u2013283.","journal-title":"Machine Learning"},{"key":"5162_CR10","first-page":"115","volume-title":"Proceedings of the 12th international conference on machine learning","author":"W. W. Cohen","year":"1995","unstructured":"Cohen,\u00a0W. W. (1995). Fast effective rule induction. In A.\u00a0Prieditis & S.\u00a0Russell (Eds.), Proceedings of the 12th international conference on machine learning (pp.\u00a0115\u2013123). Tahoe City, CA, July 9\u201312, 1995. San Mateo: Morgan Kaufmann."},{"key":"5162_CR11","first-page":"1","volume":"7","author":"J. Demsar","year":"2006","unstructured":"Demsar,\u00a0J. (2006). Statistical comparisons of classifiers over multiple datasets. Journal of Machine Learning Research, 7, 1\u201330.","journal-title":"Journal of Machine Learning Research"},{"key":"5162_CR12","first-page":"1889\u20131918","volume":"6","author":"R.-E. Fan","year":"2005","unstructured":"Fan,\u00a0R.-E., Chen,\u00a0P.-H., Lin,\u00a0C.-J., & Joachims,\u00a0T. (2005). Working set selection using the second order information for training SVM. Journal of Machine Learning Research, 6, 1889\u20131918.","journal-title":"Journal of Machine Learning Research"},{"key":"5162_CR13","first-page":"144","volume-title":"Proceedings of the 15th international conference on machine learning","author":"E. Frank","year":"1998","unstructured":"Frank,\u00a0E., & Witten,\u00a0I. H. (1998). Generating accurate rule sets without global optimization. In J.\u00a0Shavlik (Ed.), Proceedings of the 15th international conference on machine learning (pp.\u00a0144\u2013151). ICML-98, Madison, WI, 1998. San Mateo: Morgan Kaufmann."},{"issue":"1","key":"5162_CR14","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1023\/A:1006524209794","volume":"13","author":"J. F\u00fcrnkranz","year":"1999","unstructured":"F\u00fcrnkranz,\u00a0J. (1999). Separate-and-conquer rule learning. Artificial Intelligence Review, 13(1), 3\u201354.","journal-title":"Artificial Intelligence Review"},{"key":"5162_CR15","unstructured":"F\u00fcrnkranz,\u00a0J. (2004). Modeling rule precision. In J.\u00a0F\u00fcrnkranz (Ed.), Proceedings of the ECML\/PKDD-04 workshop on advances in inductive rule learning (pp.\u00a030\u201345). Pisa, Italy, 2004."},{"key":"5162_CR16","series-title":"Lecture notes in artificial intelligence","first-page":"122","volume-title":"Proceedings of the 7th European conference on machine learning","author":"J. F\u00fcrnkranz","year":"2004","unstructured":"F\u00fcrnkranz,\u00a0J. (2004). Fossil: A robust relational learner. In F.\u00a0Bergadano & L. De\u00a0Raedt (Eds.), Lecture notes in artificial intelligence : Vol.\u00a0784. Proceedings of the 7th European conference on machine learning (pp.\u00a0122\u2013137). ECML-94, Catania, Italy, 1994. Berlin: Springer."},{"issue":"2","key":"5162_CR17","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1023\/A:1007329424533","volume":"27","author":"J. F\u00fcrnkranz","year":"1997","unstructured":"F\u00fcrnkranz,\u00a0J. (1997). Pruning algorithms for rule learning. Machine Learning, 27(2), 139\u2013171.","journal-title":"Machine Learning"},{"key":"5162_CR18","series-title":"Lecture notes in artificial intelligence","first-page":"123","volume-title":"Proceedings of the 15th European conference on machine learning","author":"J. F\u00fcrnkranz","year":"2004","unstructured":"F\u00fcrnkranz,\u00a0J., & Flach,\u00a0P. (2004). An analysis of stopping and filtering criteria for rule learning. In J.-F.\u00a0Boulicaut, F.\u00a0Esposito, F.\u00a0Giannotti, & D.\u00a0Pedreschi (Eds.), Lecture notes in artificial intelligence : Vol. 3201. Proceedings of the 15th European conference on machine learning (pp.\u00a0123\u2013133). ECML-04, Pisa, Italy, 2004. Berlin: Springer."},{"issue":"1","key":"5162_CR19","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/s10994-005-5011-x","volume":"58","author":"J. F\u00fcrnkranz","year":"2005","unstructured":"F\u00fcrnkranz,\u00a0J., & Flach,\u00a0P. A. (2005). ROC \u2018n\u2019 rule learning\u2014towards a better understanding of covering algorithms. Machine Learning, 58(1), 39\u201377.","journal-title":"Machine Learning"},{"key":"5162_CR20","first-page":"70","volume-title":"Proceedings of the 11th international conference on machine learning","author":"J. F\u00fcrnkranz","year":"1994","unstructured":"F\u00fcrnkranz,\u00a0J., & Widmer,\u00a0G. (1994). Incremental reduced error pruning. In W.\u00a0Cohen & H.\u00a0Hirsh (Eds.), Proceedings of the 11th international conference on machine learning (pp. 70\u201377). ML-94, New Brunswick, NJ, 1994. San Mateo: Morgan Kaufmann."},{"key":"5162_CR21","first-page":"813","volume-title":"Proceedings of the 11th international joint conference on artificial intelligence","author":"R. Holte","year":"1989","unstructured":"Holte,\u00a0R., Acker,\u00a0L., & Porter,\u00a0B. (1989). Concept learning and the problem of small disjuncts. In Proceedings of the 11th international joint conference on artificial intelligence (pp. 813\u2013818). IJCAI-89, Detroit, MI, 1989. San Mateo: Morgan Kaufmann."},{"key":"5162_CR22","doi-asserted-by":"crossref","unstructured":"Janssen,\u00a0F., & F\u00fcrnkranz,\u00a0J. (2007). On meta-learning rule learning heuristics. In Proceedings of the 7th IEEE conference on data mining (pp. 529\u2013534). ICDM-07, Omaha, NE, 2007.","DOI":"10.1109\/ICDM.2007.51"},{"key":"5162_CR23","first-page":"40","volume-title":"Proceedings of the 11th international conference on discovery science","author":"F. Janssen","year":"2008","unstructured":"Janssen,\u00a0F., & F\u00fcrnkranz,\u00a0J. (2008). An empirical investigation of the trade-off between consistency and coverage in rule learning heuristics. In T.\u00a0Horvath, J.-F.\u00a0Boulicaut, & M.\u00a0Berthold (Eds.), Proceedings of the 11th international conference on discovery science (pp. 40\u201351). DS-08, Budapest, Hungary, 2008. Berlin: Springer."},{"key":"5162_CR24","doi-asserted-by":"crossref","unstructured":"Janssen,\u00a0F., & F\u00fcrnkranz,\u00a0J. (2009). A re-evaluation of the over-searching phenomenon in inductive rule learning. In Proceedings of the SIAM international conference on data mining (pp. 329\u2013340). SDM-09, Sparks, NV, 2009.","DOI":"10.1137\/1.9781611972795.29"},{"key":"5162_CR25","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1002\/int.4550070707","volume":"7","author":"W. Kl\u00f6sgen","year":"1992","unstructured":"Kl\u00f6sgen,\u00a0W. (1992). Problems for knowledge discovery in databases and their treatment in the statistics interpreter explora. International Journal of Intelligent Systems, 7, 649\u2013673.","journal-title":"International Journal of Intelligent Systems"},{"key":"5162_CR26","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1007\/3-540-48751-4_17","volume-title":"Proceedings of the 9th international workshop on inductive logic programming (ILP-99)","author":"N. Lavra\u010d","year":"1999","unstructured":"Lavra\u010d,\u00a0N., Flach,\u00a0P., & Zupan,\u00a0B. (1999). Rule evaluation measures: a unifying view. In S.\u00a0D\u017eeroski & P.\u00a0Flach (Eds.), Proceedings of the 9th international workshop on inductive logic programming (ILP-99) (pp. 174\u2013185). Berlin: Springer."},{"key":"5162_CR27","first-page":"153","volume":"5","author":"N. Lavra\u010d","year":"2004","unstructured":"Lavra\u010d,\u00a0N., Kav\u0161ek,\u00a0B., Flach,\u00a0P., & Todorovski,\u00a0L. (2004). Subgroup discovery with CN2-SD. Journal of Machine Learning Research, 5, 153\u2013188.","journal-title":"Journal of Machine Learning Research"},{"key":"5162_CR28","unstructured":"Lavra\u010d,\u00a0N., Cestnik,\u00a0B., & D\u017eeroski,\u00a0S. (1992a). Search heuristics in empirical inductive logic programming. In Logical approaches to machine learning, workshop notes of the 10th European conference on AI, Vienna, Austria, 1992."},{"key":"5162_CR29","unstructured":"Lavra\u010d,\u00a0N., Cestnik,\u00a0B., & D\u017eeroski,\u00a0S. (1992b). Use of heuristics in empirical inductive logic programming. In S.\u00a0H. Muggleton & K. Furukawa (Eds.), Proceedings of the 2nd international workshop on inductive logic programming (ILP-92), Number TM-1182 in ICOT Technical Memorandum, Tokyo, Japan, 1992. Institute for New Generation Computer Technology."},{"key":"5162_CR30","unstructured":"Michalski,\u00a0R. S. (1969). On the quasi-minimal solution of the covering problem. In Proceedings of the 5th international symposium on information processing (pp. 125\u2013128). Switching Circuits, Vol.\u00a0A3, FCIP-69, Bled, Yugoslavia, 1969."},{"key":"5162_CR31","first-page":"319","volume":"3","author":"J. Mingers","year":"1989","unstructured":"Mingers,\u00a0J. (1989). An empirical comparison of selection measures for decision-tree induction. Machine Learning, 3, 319\u2013342.","journal-title":"Machine Learning"},{"key":"5162_CR32","unstructured":"Mozina,\u00a0M., Dem\u0161ar,\u00a0J., Zabkar,\u00a0J., & Bratko,\u00a0I. (2006). Why is rule learning optimistic and how to correct it. In Machine learning: ECML 2006, 17th European conference on machine learning (pp. 330\u2013340)."},{"issue":"3, 4","key":"5162_CR33","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/BF03037227","volume":"13","author":"S. H. Muggleton","year":"1995","unstructured":"Muggleton,\u00a0S. H. (1995). Inverse entailment and Progol. New Generation Computing, 13(3, 4), 245\u2013286. Special issue on inductive logic programming.","journal-title":"New Generation Computing"},{"key":"5162_CR34","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1613\/jair.308","volume":"5","author":"J. Quinlan","year":"1996","unstructured":"Quinlan,\u00a0J. (1996). Learning first-order definitions of functions. Journal of Artificial Intelligence Research, 5, 139\u2013161.","journal-title":"Journal of Artificial Intelligence Research"},{"key":"5162_CR35","first-page":"239","volume":"5","author":"J. R. Quinlan","year":"1990","unstructured":"Quinlan,\u00a0J. R. (1990). Learning logical definitions from relations. Machine Learning, 5, 239\u2013266.","journal-title":"Machine Learning"},{"key":"5162_CR36","first-page":"463","volume-title":"Machine learning. An artificial intelligence approach","author":"J. R. Quinlan","year":"1983","unstructured":"Quinlan,\u00a0J. R. (1983). Learning efficient classification procedures and their application to chess end games. In R.\u00a0S.\u00a0Michalski, J.\u00a0G.\u00a0Carbonell, & T.\u00a0M.\u00a0Mitchell (Eds.), Machine learning. An artificial intelligence approach (pp. 463\u2013482). Palo Alto: Tioga."},{"key":"5162_CR37","volume-title":"Introduction to modern information retrieval","author":"G. Salton","year":"1986","unstructured":"Salton,\u00a0G., & McGill,\u00a0M. J. (1986). Introduction to modern information retrieval. New York: McGraw-Hill."},{"issue":"3","key":"5162_CR38","doi-asserted-by":"crossref","first-page":"381","DOI":"10.3233\/IDA-2005-9405","volume":"9","author":"T. Scheffer","year":"2005","unstructured":"Scheffer,\u00a0T. (2005). Finding association rules that trade support optimally against confidence. Intelligent Data Analysis, 9(3), 381\u2013395.","journal-title":"Intelligent Data Analysis"},{"key":"5162_CR39","doi-asserted-by":"crossref","unstructured":"Tan,\u00a0P.-N., Kumar,\u00a0V., & Srivastava,\u00a0J. (2002). Selecting the right interestingness measure for association patterns. In Proceedings of the 8th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 32\u201341). KDD-02, Edmonton, Alberta, 2002.","DOI":"10.1145\/775047.775053"},{"key":"5162_CR40","unstructured":"Thiel,\u00a0M. (2005). Separate and Conquer Framework und disjunktive Regeln. Master\u2019s thesis, TU Darmstadt, 2005. In German (English title: Separate and conquer framework and disjunctive rules)."},{"key":"5162_CR41","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/3-540-45372-5_25","volume-title":"4th European conference on principles of data mining and knowledge discovery (PKDD2000)","author":"L. Todorovski","year":"2000","unstructured":"Todorovski,\u00a0L., Flach,\u00a0P., & Lavrac,\u00a0N. (2000). Predictive performance of weighted relative accuracy. In D.\u00a0A.\u00a0Zighed, J.\u00a0Komorowski, & J.\u00a0Zytkow (Eds.), 4th European conference on principles of data mining and knowledge discovery (PKDD2000) (pp. 255\u2013264). Berlin: Springer."},{"issue":"5","key":"5162_CR42","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1162\/neco.1994.6.5.851","volume":"6","author":"V. Vapnik","year":"1994","unstructured":"Vapnik,\u00a0V., Levin,\u00a0E., & Cun,\u00a0Y. L. (1994). Measuring the VC-dimension of a learning machine. Neural Computation, 6(5), 851\u2013876.","journal-title":"Neural Computation"},{"key":"5162_CR43","volume-title":"Data mining\u2014practical machine learning tools and techniques with java implementations","author":"I. H. Witten","year":"2005","unstructured":"Witten,\u00a0I. H., & Frank,\u00a0E. (2005). Data mining\u2014practical machine learning tools and techniques with java implementations (2nd edn.). San Mateo: Morgan Kaufmann. http:\/\/www.cs.waikato.ac.nz\/~ml\/weka\/ .","edition":"2"},{"key":"5162_CR44","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1007\/3-540-63223-9_108","volume-title":"Proc. first European symposium on principles of data mining and knowledge discovery","author":"S. Wrobel","year":"1997","unstructured":"Wrobel,\u00a0S. (1997). An algorithm for multi-relational discovery of subgroups. In J.\u00a0Komorowski & J.\u00a0Zytkow (Eds.), Proc. first European symposium on principles of data mining and knowledge discovery (pp. 78\u201387). PKDD-97, Berlin, 1997. Berlin: Springer."},{"key":"5162_CR45","first-page":"621","volume-title":"Proceedings of the 11th European symposium on principles of data mining and knowledge discovery","author":"T. Wu","year":"2007","unstructured":"Wu,\u00a0T., Chen,\u00a0Y., & Han,\u00a0J. (2007). Association mining in large databases: a\u00a0re-examination of its measures. In Proceedings of the 11th European symposium on principles of data mining and knowledge discovery (pp. 621\u2013628). PKDD-07, Warsaw, Poland, 2007. Berlin: Springer."},{"key":"5162_CR46","doi-asserted-by":"crossref","unstructured":"Xiong,\u00a0H., Shekhar,\u00a0S., Tan,\u00a0P.-N., & Kumar,\u00a0V. (2004). Exploiting a support-based upper bound of Pearson\u2019s correlation coefficient for efficiently identifying strongly correlated pairs. In Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 334\u2013343). KDD-04, Seattle, USA, 2004.","DOI":"10.1145\/1014052.1014090"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-009-5162-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10994-009-5162-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-009-5162-2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T01:40:28Z","timestamp":1559353228000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10994-009-5162-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2009,12,9]]},"references-count":46,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2010,3]]}},"alternative-id":["5162"],"URL":"https:\/\/doi.org\/10.1007\/s10994-009-5162-2","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2009,12,9]]}}}