{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T16:56:31Z","timestamp":1774025791779,"version":"3.50.1"},"reference-count":78,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2013,1,1]],"date-time":"2013-01-01T00:00:00Z","timestamp":1356998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2013,1]]},"abstract":"<jats:p>The supervised classification of XML documents by structure involves learning predictive models in which certain structural regularities discriminate the individual document classes. Hitherto, research has focused on the adoption of prespecified substructures. This is detrimental for classification effectiveness, since the a priori chosen substructures may not accord with the structural properties of the XML documents. Therein, an unexplored question is how to choose the type of structural regularity that best adapts to the structures of the available XML documents.<\/jats:p>\n          <jats:p>We tackle this problem through X-Class, an approach that handles all types of tree-like substructures and allows for choosing the most discriminatory one. Algorithms are designed to learn compact rule-based classifiers in which the chosen substructures discriminate the classes of XML documents.<\/jats:p>\n          <jats:p>X-Class is studied across various domains and types of substructures. Its classification performance is compared against several rule-based and SVM-based competitors. Empirical evidence reveals that the classifiers induced by X-Class are compact, scalable, and at least as effective as the established competitors. In particular, certain substructures allow the induction of very compact classifiers that generally outperform the rule-based competitors in terms of effectiveness over all chosen corpora of XML data. Furthermore, such classifiers are substantially as effective as the SVM-based competitor, with the additional advantage of a high-degree of interpretability.<\/jats:p>","DOI":"10.1145\/2414782.2414785","type":"journal-article","created":{"date-parts":[[2013,2,5]],"date-time":"2013-02-05T13:19:41Z","timestamp":1360070381000},"page":"1-40","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["X-Class"],"prefix":"10.1145","volume":"31","author":[{"given":"Gianni","family":"Costa","sequence":"first","affiliation":[{"name":"ICAR-CNR"}]},{"given":"Riccardo","family":"Ortale","sequence":"additional","affiliation":[{"name":"ICAR-CNR"}]},{"given":"Ettore","family":"Ritacco","sequence":"additional","affiliation":[{"name":"ICAR-CNR"}]}],"member":"320","published-online":{"date-parts":[[2013,1]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281201"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the International Conference on Very Large Data Bases. 487--499","author":"Agrawal R."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553377"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1228268.1228271"},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the IEEE International Conference on Data Mining. 19--26","author":"Antonie M.-L."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1008694.1008705"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150461"},{"key":"e_1_2_1_8_1","unstructured":"Baeza-Yates R. and Ribeiro-Neto B. 1999. Modern Information Retrieval. Addison-Wesley Boston MA.   Baeza-Yates R. and Ribeiro-Neto B. 1999. Modern Information Retrieval . Addison-Wesley Boston MA."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1185877.1185878"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/290941.290970"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/380995.380999"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2005.06.003"},{"key":"e_1_2_1_13_1","volume-title":"Proceedings of the International Conference on Machine Learning. 103--110","author":"Brutlag J."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009715923555"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-34963-1_36"},{"key":"e_1_2_1_16_1","volume-title":"Proceedings of the 5th International Workshop of the Initiative for the Evaluation of XML Retrieval, N. Fuhr, M. Lalmas, and A. Trotman Eds., Springer, 458--472","author":"Candillier L."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"e_1_2_1_18_1","volume-title":"Proceedings of the International Conference on Data Engineering. 716--725","author":"Cheng H."},{"key":"e_1_2_1_19_1","unstructured":"Coenen F. 2004. LUCS KDD implementations of CBA and CPAR. Department of Computer Science The University of Liverpool. www.csc.liv.ac.uk\/~frans\/KDD\/Software\/.  Coenen F. 2004. LUCS KDD implementations of CBA and CPAR. Department of Computer Science The University of Liverpool. www.csc.liv.ac.uk\/~frans\/KDD\/Software\/."},{"key":"e_1_2_1_20_1","volume-title":"Proceedings of the Conference on Neural Information Processing Systems. 625--632","author":"Collins M."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.3115\/1073083.1073128"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/1007568.1007587"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI.2012.60"},{"key":"e_1_2_1_24_1","volume-title":"Proceedings of the International Conference on Principles and Practice of Knowledge Discovery in Databases. 137--148","author":"Costa G."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI.2011.24"},{"key":"e_1_2_1_26_1","volume-title":"Proceedings of the International Conference on Knowledge Discovery and Information Retrieval. 104--113","author":"Costa G."},{"key":"e_1_2_1_27_1","volume-title":"Proceedings of the International Conference on Very Large Databases. 109--118","author":"Crescenzi V."},{"key":"e_1_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Cristianini N. and Shawe-Taylor J. 2000. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press.   Cristianini N. and Shawe-Taylor J. 2000. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods . Cambridge University Press.","DOI":"10.1017\/CBO9780511801389"},{"key":"e_1_2_1_29_1","unstructured":"Da San Martino G. 2009. Kernel methods for tree structured data. Ph.D. dissertation University of Bologna Padova.  Da San Martino G. 2009. Kernel methods for tree structured data. Ph.D. dissertation University of Bologna Padova."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2004.11.009"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-85902-4_18"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/1273221.1273230"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/1394251.1394255"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.27"},{"key":"e_1_2_1_35_1","unstructured":"Fox C. 1992. Lexical Analysis and Stoplists. Prentice Hall Upper Saddle River NJ.  Fox C. 1992. Lexical Analysis and Stoplists . Prentice Hall Upper Saddle River NJ."},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-34963-1_35"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335372"},{"key":"e_1_2_1_38_1","unstructured":"Haussler D. 1999. Convolution kernels on discrete structures. Tech. rep. University of California at Santa Cruz.  Haussler D. 1999. Convolution kernels on discrete structures. Tech. rep. University of California at Santa Cruz."},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/5254.708428"},{"key":"e_1_2_1_40_1","volume-title":"Proceedings of the International Conference on Very Large Data Base. 1022--1032","author":"Helmer S.","year":"2007"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1214\/009053607000000677"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2005.860832"},{"key":"e_1_2_1_43_1","volume-title":"Proceedings of the Conference on Advances in Neural Information Processing Systems. 487--493","author":"Jaakkola T."},{"key":"e_1_2_1_44_1","volume-title":"Proceedings of the International Conference on Machine Learning. 291--298","author":"Kashima H."},{"key":"e_1_2_1_45_1","volume-title":"Proceedings of the 5th International Workshop of the Initiative for the Evaluation of XML Retrieval, N. Fuhr, M. Lalmas, and A. Trotman Eds., Springer, 485--496","author":"Knijf J. D.","year":"2007"},{"key":"e_1_2_1_46_1","first-page":"292","article-title":"Information and media technologies","volume":"2","author":"Kuboyama T.","year":"2007","journal-title":"Ann. Stat."},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1007\/11730262_6"},{"key":"e_1_2_1_48_1","volume-title":"Proceedings of the IEEE International Conference on Data Mining. 369--376","author":"Li W."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2004.1264824"},{"key":"e_1_2_1_50_1","volume-title":"Proceedings of the ACM SIGKDD International Conference on Kwnoledge Discovery and Data Mining. 80--86","author":"Liu B."},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.5555\/645804.669837"},{"key":"e_1_2_1_52_1","doi-asserted-by":"crossref","unstructured":"Manning C. Raghavan P. and Sch\u00fctze. H. 2008. Introduction to Information Retrieval. Cambridge University Press.   Manning C. Raghavan P. and Sch\u00fctze. H. 2008. Introduction to Information Retrieval . Cambridge University Press.","DOI":"10.1017\/CBO9780511809071"},{"key":"e_1_2_1_53_1","unstructured":"Mitchell T. 1997. Machine Learning. McGraw-Hill New York NY.   Mitchell T. 1997. Machine Learning . McGraw-Hill New York NY."},{"key":"e_1_2_1_54_1","volume-title":"Proceedings of the INitiative for the Evaluation of XML Retrieval (INEX\u201907)","author":"Murugeshan M."},{"key":"e_1_2_1_55_1","unstructured":"Ning P. Steinbach M. and Kumar V. 2006. Introduction to Data Mining. Addison Wesley Boston MA.  Ning P. Steinbach M. and Kumar V. 2006. Introduction to Data Mining . Addison Wesley Boston MA."},{"key":"e_1_2_1_56_1","unstructured":"Pearl J. 1998. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan and Kaufmann Burlington MA.   Pearl J. 1998. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference . Morgan and Kaufmann Burlington MA."},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1108\/eb046814"},{"key":"e_1_2_1_58_1","volume-title":"Proceedings of the European Conference on Machine Learning. 3--20","author":"Quinlan J."},{"key":"e_1_2_1_59_1","volume-title":"Kernels: Support Vector Machines, Regularization, Optimization and Beyond","author":"Sch\u00f6lkopf B.","year":"2001"},{"key":"e_1_2_1_60_1","unstructured":"Steinwart I. and Christmann A. 2008. Support Vector Machines. Springer Berlin.   Steinwart I. and Christmann A. 2008. Support Vector Machines . Springer Berlin."},{"key":"e_1_2_1_61_1","volume-title":"Proceedings of the Conference on Advances in Neural Information Processing Systems. 1321--1328","author":"Suzuki J."},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0269888907001026"},{"key":"e_1_2_1_63_1","volume-title":"Proceedings of the WebDB Workshop. 1--6.","author":"Theobald M."},{"key":"e_1_2_1_64_1","unstructured":"Vapnik V. 1998. Statistical Learning Theory. John Wiley & Sons Hoboken NJ.  Vapnik V. 1998. Statistical Learning Theory . John Wiley & Sons Hoboken NJ."},{"key":"e_1_2_1_65_1","volume-title":"Proceedings of the Conference on Advances in Neural Information Processing Systems. 569--576","author":"Vishwanathan S."},{"key":"e_1_2_1_66_1","unstructured":"Walpole R. Myers R. Myers S. and Ye K. 2011. Probability & Statistics forEngineers & Scientists. Prentice Hall Upper Saddle River NJ.  Walpole R. Myers R. Myers S. and Ye K. 2011. Probability & Statistics forEngineers & Scientists . Prentice Hall Upper Saddle River NJ."},{"key":"e_1_2_1_67_1","volume-title":"Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. 441--451","author":"Wang C."},{"key":"e_1_2_1_68_1","volume-title":"Proceedings of the SIAM International Conference on Data Mining. 205--216","author":"Wang J."},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(05)80023-1"},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1145\/1451940.1451960"},{"key":"e_1_2_1_71_1","volume-title":"Proceedings of the 5th International Workshop of the Initiative for the Evaluation of XML Retrieval, N. Fuhr, M. Lalmas, and A. Trotman Eds., Springer, 444--457","author":"Xing G."},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02948828"},{"key":"e_1_2_1_73_1","volume-title":"Proceedings of the 8th International Workshop of the Initiative for the Evaluation of XML Retrieval, S. Geva, J. Kamps, and A. Trotman Eds., Springer, 441--448","author":"Yang J."},{"key":"e_1_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-85902-4_21"},{"key":"e_1_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1145\/347090.347164"},{"key":"e_1_2_1_76_1","volume-title":"Proceedings of the SIAM International Conference on Data Mining. 331--335","author":"Yin X."},{"key":"e_1_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.125"},{"key":"e_1_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-006-5832-2"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2414782.2414785","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2414782.2414785","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T09:21:23Z","timestamp":1750238483000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2414782.2414785"}},"subtitle":["Associative Classification of XML Documents by Structure"],"short-title":[],"issued":{"date-parts":[[2013,1]]},"references-count":78,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2013,1]]}},"alternative-id":["10.1145\/2414782.2414785"],"URL":"https:\/\/doi.org\/10.1145\/2414782.2414785","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,1]]},"assertion":[{"value":"2011-01-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2012-10-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2013-01-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}