A process for predicting manhole events in Manhattan

Machine Learning

Volumn 80, Issue 1, 2010, Pages 1-31

  Rudin, Cynthia ^a   Passonneau, Rebecca J ^b   Radeva, Axinia ^b   Dutta, Haimonti ^b   Ierome, Steve ^c   Isaac, Delfina ^c  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b Och Spine at New York Presbyterian Hospitals   (United States)

^c Consolidated Edison Company of New York ^*  (United States)

Author keywords

Applications of machine learning; Knowledge discovery; Manhole events; Ranking

Indexed keywords

ELECTRICAL GRIDS; EVENT PREDICTION; KNOWLEDGE DISCOVERY; KNOWLEDGE DISCOVERY AND DATA MININGS; MACHINE-LEARNING; MANHATTANS; MANHOLE EVENTS; NEW APPLICATIONS; PRE-PROCESSING; PRIORITIZATION; REAL-WORLD; REPAIR WORKS;

DATA MINING; LEARNING SYSTEMS; MINING;

FORECASTING;

EID: 74849137153     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-009-5166-y     Document Type: Article

References (46)

1. Azevedo, A., & Santos, M. F. (2008). KDD, SEMMA and CRISP-DM: a parallel overview. In Proceedings of the IADIS European conf. data mining (pp. 182-185).
2. Becker, H., & Arias, M. (2007). Real-time ranking with concept drift using expert advice. In Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining (KDD '07) (pp. 86-94). New York: ACM. (Pubitemid 350229195)
3. Boriah, S., Kumar, V., Steinbach, M., Potter, C., & Klooster, S. A. (2008). Land cover change detection: a case study. In Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (KDD'08) (pp. 857-865). New York: ACM.
4. A. P. Bradley 1997 The use of the area under the ROC curve in the evaluation of machine learning algorithms Pattern Recognition 30 7 1145 1159 10.1016/S0031-3203(96)00142-2
5. Castano, R., Judd, M., Anderson, R. C., & Estlin, T. (2003). Machine learning challenges in Mars rover traverse science. In Workshop on machine learning technologies for autonomous space applications, international conference on machine learning.
6. G. Chen A. T. Peterson 2002 Prioritization of areas in China for the conservation of endangered birds using modelled geographical distributions Bird Conservation International 12 197 209 10.1017/S0959270902002125 (Pubitemid 35280958)
7. H. Chen W. Chung J. J. Xu G. Wang Y. Qin M. Chau 2004 Crime data mining: a general framework and some examples IEEE Computer 37 4 50 56
8. J. Cohen 1960 A coefficient of agreement for nominal scales Educational and Psychological Measurement 20 37 46 10.1177/001316446002000104
9. Cunningham, H., Maynard, D., Bontcheva, K., & Tablan, V. (2002). GATE: a framework and graphical development environment for robust NLP tools and applications. In Proceedings of the 40th anniversary meeting of the association for computational linguistics (ACL'02).
10. Devaney, M., & Ram, A. (2005). Preventing failures by mining maintenance logs with case-based reasoning. In Proceedings of the 59th meeting of the society for machinery failure prevention technology (MFPT-59).
11. M. Dudík S. J. Phillips R. E. Schapire 2007 Maximum entropy density estimation with generalized regularization and an application to species distribution modeling Journal of Machine Learning Research 8 1217 1260
12. Dutta, H., Rudin, C., Passonneau, R., Seibel, F., Bhardwaj, N., Radeva, A., Liu, Z. A., & Ierome S, Isaac, D. (2008). Visualization of manhole and precursor-type events for the Manhattan electrical distribution system. In Proceedings of the workshop on geo-visualization of dynamics, movement and change, 11th AGILE international conference on geographic information science, Girona, Spain.
13. U. Fayyad R. Uthurusamy 2002 Evolving data into mining solutions for insights Communications of the ACM 45 8 28 31 10.1145/545151.545174
14. U. Fayyad G. Piatetsky-Shapiro P. Smyth 1996 From data mining to knowledge discovery in databases AI Magazine 17 37 54
15. W. J. Frawley G. Piatetsky-Shapiro C. J. Matheus 1992 Knowledge discovery in databases: an overview AI Magazine 13 3 57 70
16. Y. Freund R. Iyer R. E. Schapire Y. Singer 2003 An efficient boosting algorithm for combining preferences Journal of Machine Learning Research 4 933 969 10.1162/jmlr.2003.4.6.933 2125342
17. Google Earth (2009). http://www.earth.google.com.
18. Grishman, R., Hirschman, L., & Nhan, N. T. (1986). Discovery procedures for sublanguage selectional patterns: initial experiments. Computational Linguistics, 205-215.
19. Gross, P., Boulanger, A., Arias, M., Waltz, D. L., Long, P. M., Lawson, C., Anderson, R., Koenig, M., Mastrocinque, M., Fairechio, W., Johnson, J. A., Lee, S., Doherty, F., & Kressner, A. (2006). Predicting electricity distribution feeder failures using machine learning susceptibility analysis. In Proceedings of the eighteenth conference on innovative applications of artificial intelligence IAAI-06, Boston, Massachusetts.
20. I. Guyon A. Elisseeff 2003 An introduction to variable and feature selection Journal of Machine Learning Research 3 1157 1182 1102.68556 10.1162/153244303322753616
21. D. J. Hand 1994 Deconstructing statistical questions Journal of the Royal Statistical Society Series A (Statistics in Society) 157 3 317 356 10.2307/2983526 1287128
22. J. A. Harding M. Shahbaz Srinivas A. Kusiak 2006 Data mining in manufacturing: a review Journal of Manufacturing Science and Engineering 128 4 969 976 10.1115/1.2194554 (Pubitemid 44770144)
23. Harris, Z. (1982). Discourse and sublanguage. In Kittredge, R., & Lehrberger, J. (Eds.) Sublanguage: studies of language in restricted semantic domains (pp. 231-236). Berlin: de Gruyter.
24. Hirschman, L., Palmer, M., Dowding, J., Dahl, D., Linebarger, M., Passonneau, R., Lang, F., Ball, C., & Weir, C. (1989). The PUNDIT natural-language processing system. In Proceedings of the annual AI systems in government conference (pp. 234-243).
25. Hsu, W., Lee, M. L., Liu, B., & Ling, T. W. (2000). Exploration mining in diabetic patients databases: findings and conclusions. In Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining (KDD '00) (pp. 430-436). New York: ACM.
26. R. Jiang H. Yang L. Zhou C. C. J. Kuo F. Sun T. Chen 2007 Sequence-based prioritization of nonsynonymous single-nucleotide polymorphisms for the study of disease mutations American Journal of Human Genetics 81 2 346 360 10.1086/519747 (Pubitemid 47236081)
27. J. Kirtley Jr. W. Hagman B. Lesieutre M. Boyd E. Warren H. Chou R. Tabors 1996 Monitoring the health of power transformers IEEE Computer Applications in Power 9 1 18 23 10.1109/67.475955
28. Kittredge, R. (1982). Sublanguages. American Journal of Computational Linguistics, 79-84.
29. R. Kittredge T. Korelsky O. Rambow 1991 On the need for domain communication knowledge Computational Intelligence 7 4 305 314 10.1111/j.1467-8640.1991.tb00403.x
30. R. Kohavi G. John 1997 Wrappers for feature selection Artificial Intelligence 97 1-2 273 324 0904.68143 10.1016/S0004-3702(97)00043-X
31. Krippendorff, K. (1980). Content analysis: an introduction to its methodology. Beverly Hills: Sage.
32. A. Kusiak S. Shah 2006 A data-mining-based system for prediction of water chemistry faults IEEE Transactions on Industrial Electronics 53 2 593 603 10.1109/TIE.2006.870706
33. Liddy, E. D., Symonenko, S., & Rowe, S. (2006). Sublanguage analysis applied to trouble tickets. In Proceedings of the Florida artificial intelligence research society conference (pp. 752-757).
34. Linebarger, M., Dahl, D., Hirschman, L., & Passonneau, R. (1988). Sentence fragments regular structures. In Proceedings of the 26th association for computational linguistics, Buffalo, NY.
35. J. F. Murray G. F. Hughes K. Kreutz-Delgado 2005 Machine learning methods for predicting failures in hard drives: a multiple-instance application Journal of Machine Learning Research 6 783 816 2249839
36. National Institute of Standards and Technology (NIST), Information Access Division (ACE) Automatic Content Extraction Evaluation. http://www.itl.nist. gov/iad/mig/tests/ace/.
37. N. Oza J. P. Castle J. Stutz 2009 Classification of aeronautics system health and safety documents IEEE Transactions on Systems, Man and Cybernetics, Part C 39 670 680 10.1109/TSMCC.2009.2020788
38. Passonneau, R., Rudin, C., Radeva, A., & Liu, Z. A. (2009). Reducing noise in labels and features for a real world dataset: application of NLP corpus annotation methods. In Proceedings of the 10th international conference on computational linguistics and intelligent text processing (CICLing).
39. Patel, K., Fogarty, J., Landay, J. A., & Harrison, B. (2008). Investigating statistical machine learning as a tool for software development. In Proceedings of ACM CHI 2008 conference on human factors in computing systems (CHI 2008) (pp. 667-676).
40. Radeva, A., Rudin, C., Passonneau, R., & Isaac, D. (2009). Report cards for manholes: eliciting expert feedback for a machine learning task. In Proceedings of the international conference on machine learning and applications.
41. C. Rudin 2009 The P-Norm Push: a simple convex ranking algorithm that concentrates at the top of the list Journal of Machine Learning Research 10 2233 2271
42. Sager, N. (1970). The sublanguage method in string grammars. In R. W. Ewton Jr. & J. Ornstein (Eds.), Studies in language and linguistics, University of Texas at El Paso (pp. 89-98).
43. Steed, J. (1995). Condition monitoring applied to power transformers-an REC view. In Second international conference on the reliability of transmission and distribution equipment (pp. 109-114).
44. Symonenko, S., Rowe, S., & Liddy, E. D. (2006). Illuminating trouble tickets with sublanguage theory. In Proceedings of the human language technology/North American association of computational linguistics conference.
45. Vilalta, R., & Ma, S. (2002). Predicting rare events in temporal domains. In IEEE international conference on data mining (pp. 474-481).
46. Weiss, G. M., & Hirsh, H. (2000). Learning to predict extremely rare events. In AAAI workshop on learning from imbalanced data sets (pp. 64-68). Menlo Park: AAAI Press.