Multiple instance learning for group record linkage

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7301 LNAI, Issue PART 1, 2012, Pages 171-182

  Fu, Zhichun ^a   Zhou, Jun ^a   Christen, Peter ^a   Boot, Mac ^a  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

entity resolution; historical census data; instance classification; Multiple instance learning; record linkage

Indexed keywords

ACROSS TIME; CENSUS DATA; DATA SOURCE; LINKED DATUM; MULTIPLE INSTANCE LEARNING; MULTIPLE-GROUP; OVERALL QUALITY; RECORD LINKAGE; RESEARCH EFFORTS; SYNTHETIC DATA;

DATA MINING; LEARNING SYSTEMS; POPULATION STATISTICS;

DATA HANDLING;

EID: 84861452098     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-30217-6_15     Document Type: Conference Paper

References (24)

1. Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: NIPS, Vancouver, Canada, pp. 561-568 (2003)
2. Bilenko, M., Mooney, R.J.: Adaptive duplicate detection using learnable string similarity measures. In: ACM KDD, Washington, DC, pp. 39-48 (2003)
3. Bloothooft, G.: Multi-source family reconstruction. History and Computing 7(2), 90-103 (1995)
4. Chen, Y., Bi, J., Wang, J.: MILES: Multiple-instance learning via embedded instance selection. IEEE TPAMI 28(12), 1931-1947 (2006)
5. Chen, Y., Wang, J.Z.: Image categorization by learning and reasoning with regions. Journal of Machine Learning Research 5 (2004)
6. Christen, P.: Automatic Training Example Selection for Scalable Unsupervised Record Linkage. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 511-518. Springer, Heidelberg (2008)
7. Christen, P.: Development and user experiences of an open source data cleaning, deduplication and record linkage system. ACM SIGKDD Explorations 11(1), 39-48 (2009)
8. Christen, P.: A survey of indexing techniques for scalable record linkage and deduplication. IEEE TKDE (2011)
9. Dietterich, T.G., Lathrop, R.H., Lozano-Perez, T.: Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence 89, 31-71 (1997)
10. Elmagarmid, A.K., Ipeirotis, P.G., Verykios, V.S.: Duplicate record detection: A survey. IEEE Transactions on Knowledge and Data Engineering 19(1), 1-16 (2007)
11. Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: LIBLINEAR: A library for large linear classification. JMLR 9, 1871-1874 (2008)
12. Fu, Z., Christen, P., Boot, M.: Automatic cleaning and linking of historical census data using household information. In: IEEE ICDM Workshop on DDDM (2011)
13. Fu, Z., Christen, P., Boot, M.: A supervised learning and group linking method for historical census household linkage. In: AusDM (2011)
14. Fu, Z., Robles-Kelly, A., Zhou, J.: MILIS: Multiple instance learning with instance selection. IEEE TPAMI 33(5), 958-977 (2011)
15. Fure, E.: Interactive record linkage: The cumulative construction of life courses. Demographic Research 3, 11 (2000)
16. Li, F., Sminchisescu, C.: Convex multiple instance learning by estimating likelihood ratio. In: NIPS (2010)
17. On, B.-W., Elmaciogl, E., Lee, D., Kang, J., Pei, J.: Improving grouped-entity resolution using quasi-cliques. In: IEEE ICDM, Hong Kong, pp. 1008-1015 (2006)
18. On, B.-W., Koudas, N., Lee, D., Srivastava, D.: Group linkage. In: IEEE ICDE, Istanbul, Turkey, pp. 496-505 (2007)
19. Quass, D., Starkey, P.: Record linkage for genealogical databases. In: ACM KDD Workshop, Washington, DC, pp. 40-42 (2003)
20. Reid, A., Davies, R., Garrett, E.: Nineteenth century Scottish demography from linked censuses and civil registers: a 'sets of related individuals' approach. History and Computing 14(1+2), 61-86 (2006)
21. Ruggles, S.: Linking historical censuses: a new approach. History and Computing 14(1+2), 213-224 (2006)
22. Tan, P., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Addison- Wesley (2005)
23. Vapnik, V.: The Nature of Statistical Learning Theory. Springer (1995)
24. Xiao, Y., Liu, B., Cao, L., Yin, J., Wu, X.: SMILE: A similarity-based approach for multiple instance learning. In: IEEE ICDM, Sydney, pp. 309-313 (2010)