{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T05:41:39Z","timestamp":1776404499286,"version":"3.51.2"},"publisher-location":"Berlin, Heidelberg","reference-count":50,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"value":"9783662622704","type":"print"},{"value":"9783662622711","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-662-62271-1_5","type":"book-chapter","created":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T14:39:15Z","timestamp":1599748755000},"page":"132-159","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Evaluating Classification Feasibility Using Functional Dependencies"],"prefix":"10.1007","author":[{"given":"Marie","family":"Le Guilly","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean-Marc","family":"Petit","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vasile-Marian","family":"Scuturici","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,10]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Abo Khamis, M., Ngo, H.Q., Nguyen, X., Olteanu, D., Schleich, M.: In-database learning with sparse tensors. In: Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, pp. 325\u2013340. ACM (2018)","DOI":"10.1145\/3196959.3196960"},{"key":"5_CR2","first-page":"580","volume":"74","author":"WW Armstrong","year":"1974","unstructured":"Armstrong, W.W.: Dependency structures of database relationship. Inf. Process. 74, 580\u2013583 (1974)","journal-title":"Inf. Process."},{"key":"5_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1007\/3-540-47961-9_32","volume-title":"Advanced Information Systems Engineering","author":"J Berlin","year":"2002","unstructured":"Berlin, J., Motro, A.: Database schema matching using machine learning with feature selection. In: Pidduck, A.B., Ozsu, M.T., Mylopoulos, J., Woo, C.C. (eds.) CAiSE 2002. LNCS, vol. 2348, pp. 452\u2013466. Springer, Heidelberg (2002). \nhttps:\/\/doi.org\/10.1007\/3-540-47961-9_32"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Bilenko, M., Kamath, B., Mooney, R.J.: Adaptive blocking: learning to scale up record linkage. In: Sixth International Conference on Data Mining (ICDM 2006), pp. 87\u201396. IEEE (2006)","DOI":"10.1109\/ICDM.2006.13"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Bohannon, P., Fan, W., Geerts, F., Jia, X., Kementsietsidis, A.: Conditional functional dependencies for data cleaning. In: 2007 IEEE 23rd International Conference on Data Engineering, pp. 746\u2013755. IEEE (2007)","DOI":"10.1109\/ICDE.2007.367920"},{"key":"5_CR6","unstructured":"Bonifati, A., Ciucanu, R., Staworko, S.: Interactive inference of join queries (2014)"},{"issue":"3","key":"5_CR7","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1002\/(SICI)1097-4571(199803)49:3<217::AID-ASI4>3.0.CO;2-D","volume":"49","author":"P Bosc","year":"1998","unstructured":"Bosc, P., Dubois, D., Prade, H.: Fuzzy functional dependencies and redundancy elimination. J. Am. Soc. Inf. Sci. 49(3), 217\u2013235 (1998)","journal-title":"J. Am. Soc. Inf. Sci."},{"key":"5_CR8","series-title":"International Centre for Mechanical Sciences","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/978-3-7091-2668-4_10","volume-title":"Learning, Networks and Statistics","author":"I Bratko","year":"1997","unstructured":"Bratko, I.: Machine learning: between accuracy and interpretability. In: Della Riccia, G., Lenz, H.-J., Kruse, R. (eds.) Learning, Networks and Statistics. ICMS, vol. 382, pp. 163\u2013177. Springer, Vienna (1997). \nhttps:\/\/doi.org\/10.1007\/978-3-7091-2668-4_10"},{"issue":"1","key":"5_CR9","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1109\/TKDE.2015.2472010","volume":"28","author":"L Caruccio","year":"2015","unstructured":"Caruccio, L., Deufemia, V., Polese, G.: Relaxed functional dependencies\u2013a survey of approaches. IEEE Trans. Knowl. Data Eng. 28(1), 147\u2013165 (2015)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"5_CR10","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/0022-0000(84)90075-8","volume":"28","author":"MA Casanova","year":"1984","unstructured":"Casanova, M.A., Fagin, R., Papadimitriou, C.H.: Inclusion dependencies and their interaction with functional dependencies. J. Comput. Syst. Sci. 28(1), 29\u201359 (1984)","journal-title":"J. Comput. Syst. Sci."},{"issue":"12","key":"5_CR11","doi-asserted-by":"publisher","first-page":"1666","DOI":"10.1109\/TKDE.2007.190651","volume":"19","author":"SK Chang","year":"2007","unstructured":"Chang, S.K., Deufemia, V., Polese, G., Vacca, M.: A normalization framework for multimedia databases. IEEE Trans. Knowl. Data Eng. 19(12), 1666\u20131679 (2007)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"5_CR12","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.tcs.2016.11.004","volume":"658","author":"B Chardin","year":"2017","unstructured":"Chardin, B., Coquery, E., Pailloux, M., Petit, J.: RQL: a query language for rule discovery in databases. Theoret. Comput. Sci. 658, 357\u2013374 (2017). \nhttps:\/\/doi.org\/10.1016\/j.tcs.2016.11.004","journal-title":"Theoret. Comput. Sci."},{"issue":"11","key":"5_CR13","doi-asserted-by":"publisher","first-page":"864","DOI":"10.14778\/2983200.2983203","volume":"9","author":"X Chu","year":"2016","unstructured":"Chu, X., Ilyas, I.F., Koutris, P.: Distributed data deduplication. Proc. VLDB Endow. 9(11), 864\u2013875 (2016)","journal-title":"Proc. VLDB Endow."},{"issue":"13","key":"5_CR14","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.14778\/2536258.2536262","volume":"6","author":"X Chu","year":"2013","unstructured":"Chu, X., Ilyas, I.F., Papotti, P.: Discovering denial constraints. Proc. VLDB Endow. 6(13), 1498\u20131509 (2013)","journal-title":"Proc. VLDB Endow."},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Dalkilic, M.M., Roberston, E.L.: Information dependencies. In: Proceedings of the Nineteenth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 245\u2013253. ACM (2000)","DOI":"10.1145\/335168.336059"},{"key":"5_CR16","unstructured":"Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning. arXiv preprint \narXiv:1702.08608\n\n (2017)"},{"issue":"3","key":"5_CR17","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1145\/320557.320571","volume":"2","author":"R Fagin","year":"1977","unstructured":"Fagin, R.: Multivalued dependencies and a new normal form for relational databases. ACM Trans. Database Syst. (TODS) 2(3), 262\u2013278 (1977)","journal-title":"ACM Trans. Database Syst. (TODS)"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Fan, W.: Dependencies revisited for improving data quality. In: Proceedings of the Twenty-Seventh ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 159\u2013170. ACM (2008)","DOI":"10.1145\/1376916.1376940"},{"issue":"1","key":"5_CR19","first-page":"3133","volume":"15","author":"M Fern\u00e1ndez-Delgado","year":"2014","unstructured":"Fern\u00e1ndez-Delgado, M., Cernadas, E., Barro, S., Amorim, D.: Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res. 15(1), 3133\u20133181 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"5_CR20","doi-asserted-by":"publisher","unstructured":"Getoor, L.: The power of relational learning (invited talk). In: 22nd International Conference on Database Theory, ICDT 2019, Lisbon, Portugal, 26\u201328 March 2019, pp. 2:1\u20132:1 (2019). \nhttps:\/\/doi.org\/10.4230\/LIPIcs.ICDT.2019.2","DOI":"10.4230\/LIPIcs.ICDT.2019.2"},{"key":"5_CR21","volume-title":"Data Mining: Concepts and Techniques","author":"J Han","year":"2005","unstructured":"Han, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., San Francisco (2005)"},{"issue":"2","key":"5_CR22","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1093\/comjnl\/42.2.100","volume":"42","author":"Y Huhtala","year":"1999","unstructured":"Huhtala, Y., K\u00e4rkk\u00e4inen, J., Porkka, P., Toivonen, H.: TANE: an efficient algorithm for discovering functional and approximate dependencies. Comput. J. 42(2), 100\u2013111 (1999)","journal-title":"Comput. J."},{"issue":"1","key":"5_CR23","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/0304-3975(95)00028-U","volume":"149","author":"J Kivinen","year":"1995","unstructured":"Kivinen, J., Mannila, H.: Approximate inference of functional dependencies from relations. Theoret. Comput. Sci. 149(1), 129\u2013149 (1995)","journal-title":"Theoret. Comput. Sci."},{"issue":"1","key":"5_CR24","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/0304-3975(95)00028-U","volume":"149","author":"J Kivinen","year":"1995","unstructured":"Kivinen, J., Mannila, H.: Approximate inference of functional dependencies from relations. Theoret. Comput. Sci. 149(1), 129\u2013149 (1995). \nhttps:\/\/doi.org\/10.1016\/0304-3975(95)00028-U","journal-title":"Theoret. Comput. Sci."},{"key":"5_CR25","volume-title":"Introduction to Statistical Relational Learning","author":"D Koller","year":"2007","unstructured":"Koller, D., et al.: Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)"},{"issue":"5","key":"5_CR26","doi-asserted-by":"publisher","first-page":"1847","DOI":"10.1016\/j.eswa.2012.09.017","volume":"40","author":"O Kwon","year":"2013","unstructured":"Kwon, O., Sim, J.M.: Effects of data set features on the performances of classification algorithms. Expert Syst. Appl. 40(5), 1847\u20131857 (2013). \nhttps:\/\/doi.org\/10.1016\/j.eswa.2012.09.017","journal-title":"Expert Syst. Appl."},{"key":"5_CR27","unstructured":"Lam, K.W., Lee, V.C.: Building decision trees using functional dependencies. In: 2004 Proceedings of the International Conference on Information Technology: Coding and Computing. ITCC 2004. vol. 2, pp. 470\u2013473. IEEE (2004)"},{"key":"5_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-85729-349-7","volume-title":"A Guided Tour of Relational Databases and Beyond","author":"M Levene","year":"2012","unstructured":"Levene, M., Loizou, G.: A Guided Tour of Relational Databases and Beyond. Springer, Heidelberg (2012). \nhttps:\/\/doi.org\/10.1007\/978-0-85729-349-7"},{"issue":"2","key":"5_CR29","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","volume":"28","author":"S Lloyd","year":"1982","unstructured":"Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129\u2013137 (1982)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"5_CR30","volume-title":"Foundations of Machine Learning","author":"M Mohri","year":"2018","unstructured":"Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of Machine Learning. MIT Press, Cambridge (2018)"},{"issue":"2","key":"5_CR31","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1109\/TKDE.2006.31","volume":"18","author":"C Ordonez","year":"2006","unstructured":"Ordonez, C.: Integrating k-means clustering with a relational DBMS using SQL. IEEE Trans. Knowl. Data Eng. 18(2), 188\u2013201 (2006). \nhttps:\/\/doi.org\/10.1109\/TKDE.2006.31","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"5_CR32","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"4","key":"5_CR33","first-page":"3","volume":"23","author":"E Rahm","year":"2000","unstructured":"Rahm, E., Do, H.H.: Data cleaning: problems and current approaches. IEEE Data Eng. Bull. 23(4), 3\u201313 (2000)","journal-title":"IEEE Data Eng. Bull."},{"issue":"2","key":"5_CR34","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1145\/42338.42344","volume":"13","author":"K Raju","year":"1988","unstructured":"Raju, K., Majumdar, A.K.: Fuzzy functional dependencies and lossless join decomposition of fuzzy relational database systems. ACM Trans. Database Syst. (TODS) 13(2), 129\u2013166 (1988)","journal-title":"ACM Trans. Database Syst. (TODS)"},{"issue":"11","key":"5_CR35","doi-asserted-by":"publisher","first-page":"1190","DOI":"10.14778\/3137628.3137631","volume":"10","author":"T Rekatsinas","year":"2017","unstructured":"Rekatsinas, T., Chu, X., Ilyas, I.F., R\u00e9, C.: Holoclean: holistic data repairs with probabilistic inference. Proc. VLDB Endow. 10(11), 1190\u20131201 (2017)","journal-title":"Proc. VLDB Endow."},{"key":"5_CR36","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53\u201365 (1987)","journal-title":"J. Comput. Appl. Math."},{"key":"5_CR37","doi-asserted-by":"publisher","unstructured":"Sa, C.D., Ilyas, I.F., Kimelfeld, B., R\u00e9, C., Rekatsinas, T.: A formal framework for probabilistic unclean databases. In: 22nd International Conference on Database Theory, ICDT 2019, Lisbon, Portugal, 26\u201328 March 2019, pp. 6:1\u20136:18 (2019). \nhttps:\/\/doi.org\/10.4230\/LIPIcs.ICDT.2019.6","DOI":"10.4230\/LIPIcs.ICDT.2019.6"},{"key":"5_CR38","doi-asserted-by":"crossref","unstructured":"Salimi, B., Rodriguez, L., Howe, B., Suciu, D.: Interventional fairness: causal database repair for algorithmic fairness. In: Proceedings of the 2019 International Conference on Management of Data, pp. 793\u2013810. ACM (2019)","DOI":"10.1145\/3299869.3319901"},{"issue":"4","key":"5_CR39","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1007\/s10462-015-9433-y","volume":"44","author":"G Santafe","year":"2015","unstructured":"Santafe, G., Inza, I., Lozano, J.A.: Dealing with the evaluation of supervised classification algorithms. Artif. Intell. Rev. 44(4), 467\u2013508 (2015). \nhttps:\/\/doi.org\/10.1007\/s10462-015-9433-y","journal-title":"Artif. Intell. Rev."},{"issue":"2","key":"5_CR40","doi-asserted-by":"publisher","first-page":"74","DOI":"10.9735\/0975-2927.3.2.74-88","volume":"3","author":"P Santanu","year":"2011","unstructured":"Santanu, P., Jaya, S., Das, A.K., et al.: Feature selection by attribute clustering of infected rice plant images. Int. J. Mach. Intell. 3(2), 74\u201388 (2011)","journal-title":"Int. J. Mach. Intell."},{"key":"5_CR41","doi-asserted-by":"crossref","unstructured":"Schleich, M., Olteanu, D., Ciucanu, R.: Learning linear regression models over factorized joins. In: Proceedings of the 2016 International Conference on Management of Data, pp. 3\u201318. ACM (2016)","DOI":"10.1145\/2882903.2882939"},{"issue":"3","key":"5_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2000824.2000826","volume":"36","author":"S Song","year":"2011","unstructured":"Song, S., Chen, L.: Differential dependencies: reasoning and discovery. ACM Trans. Database Syst. (TODS) 36(3), 1\u201341 (2011)","journal-title":"ACM Trans. Database Syst. (TODS)"},{"key":"5_CR43","doi-asserted-by":"crossref","unstructured":"Tumer, K., Ghosh, J.: Estimating the Bayes error rate through classifier combining. In: Proceedings of 13th International Conference on Pattern Recognition, vol. 2, pp. 695\u2013699. IEEE (1996)","DOI":"10.1109\/ICPR.1996.546912"},{"issue":"5","key":"5_CR44","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1162\/neco.1994.6.5.851","volume":"6","author":"V Vapnik","year":"1994","unstructured":"Vapnik, V., Levin, E., Cun, Y.L.: Measuring the VC-dimension of a learning machine. Neural Comput. 6(5), 851\u2013876 (1994)","journal-title":"Neural Comput."},{"key":"5_CR45","unstructured":"Wang, H., Zaniolo, C., Luo, C.R.: Atlas: a small but complete SQL extension for data mining and data streams. In: Proceedings of the 29th International Conference on Very Large Data Bases, vol. 29, pp. 1113\u20131116. VLDB Endowment (2003)"},{"key":"5_CR46","doi-asserted-by":"crossref","unstructured":"Wang, T., Rudin, C., Velez-Doshi, F., Liu, Y., Klampfl, E., MacNeille, P.: Bayesian rule sets for interpretable classification. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 1269\u20131274. IEEE (2016)","DOI":"10.1109\/ICDM.2016.0171"},{"key":"5_CR47","doi-asserted-by":"crossref","unstructured":"Wei, Z., Link, S.: DataProf: semantic profiling for iterative data cleansing and business rule acquisition. In: Proceedings of the 2018 International Conference on Management of Data, pp. 1793\u20131796. ACM (2018)","DOI":"10.1145\/3183713.3193544"},{"issue":"3","key":"5_CR48","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1111\/rssa.12227","volume":"180","author":"J Zeng","year":"2017","unstructured":"Zeng, J., Ustun, B., Rudin, C.: Interpretable classification models for recidivism prediction. J. Roy. Stat. Soc.: Ser. A (Stat. Soc.) 180(3), 689\u2013722 (2017)","journal-title":"J. Roy. Stat. Soc.: Ser. A (Stat. Soc.)"},{"issue":"5\u20136","key":"5_CR49","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1080\/713827180","volume":"17","author":"S Zhang","year":"2003","unstructured":"Zhang, S., Zhang, C., Yang, Q.: Data preparation for data mining. Appl. Artif. Intell. 17(5\u20136), 375\u2013381 (2003)","journal-title":"Appl. Artif. Intell."},{"key":"5_CR50","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1007\/11731139_75","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"B Zou","year":"2006","unstructured":"Zou, B., Ma, X., Kemme, B., Newton, G., Precup, D.: Data mining using relational database management systems. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 657\u2013667. Springer, Heidelberg (2006). \nhttps:\/\/doi.org\/10.1007\/11731139_75"}],"container-title":["Lecture Notes in Computer Science","Transactions on Large-Scale Data- and Knowledge-Centered Systems XLIV"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-662-62271-1_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T14:40:57Z","timestamp":1599748857000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-662-62271-1_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783662622704","9783662622711"],"references-count":50,"URL":"https:\/\/doi.org\/10.1007\/978-3-662-62271-1_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"10 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}