A machine-learning approach to detecting unknown bacterial serovars

Statistical Analysis and Data Mining

Volumn 3, Issue 5, 2010, Pages 289-301

  Akova, Ferit ^a   Dundar, Murat ^a   Davisson, V Jo ^b,c   Hirleman, E Daniel ^d   Bhunia, Arun K ^c   Robinson, J Paul ^c   Rajwa, Bartek ^c  

^a Purdue University School of Science   (United States)

^b PURDUE UNIVERSITY   (United States)

^c PURDUE UNIVERSITY   (United States)

^d Purdue University   (United States)

Author keywords

Anomaly detection; Bayesian classifier; Nonexhaustive training data; Novelty detection

Indexed keywords

BAYESIAN NETWORKS; CLASSIFICATION (OF INFORMATION); DATA MINING; FOOD SUPPLY; LEARNING SYSTEMS; LIGHT SCATTERING;

ANOMALY DETECTION; BACTERIAL PATHOGENS; BAYESIAN APPROACHES; BAYESIAN CLASSIFIER; MACHINE LEARNING APPROACHES; NONEXHAUSTIVE TRAINING DATA; NOVELTY DETECTION; SEROVARS; TRAINING DATA; TRAINING DATASET;

BACTERIA;

EID: 78649602781     PISSN: 19321872     EISSN: 19321864     Source Type: Journal    
DOI: 10.1002/sam.10085     Document Type: Article

References (31)

1. E. Klein, D. L. Smith, and R. Laxminarayan, Hospitalizations and deaths caused by methicillin-resistant Staphylococcus aureus, United States, 1999-2005, Emerg Infect Dis 13(12) (2007), 1840-1846. PMID: 18258033 [Online].
2. J. Heaton, and K. Jones, Microbial contamination of fruit and vegetables and the behaviour of enteropathogens in the phyllosphere: a review, J Appl Microbiol 104(3) (2008), 613-626 [Online]. http://dx.doi.org/10.1111/j.1365-2672.2007.03587.x.
3. M. T. Jay, M. Cooley, D. Carychao, et al., Escherichia coli O157:H7 in feral swine near spinach fields and cattle, central california coast, Emerg Infect Dis 13 (12) (2007), 1908-1911. PMID: 18258044 [Online].
4. P. Gerner-Smidt, and J. M. Whichard, Foodborne disease trends and reports, Foodborne Pathogens Dis 6(1) (2009), 1-5. PMID: 19182964 [Online].
5. CDC, Multistate outbreak of Salmonella infections associated with peanut butter and peanut butter-containing products-United States, 2008-2009, Morb Mortal Wkly Rep 58(4) (2009), 85-90 [Online].
6. B. Swaminathan, and P. Gerner-Smidt, "The epidemiology of human listeriosis, "Microbes and Infection vol. 9 (2007), [Online]. Available: (10), 1236-1243. Aug.
7. F. Ligler, C. Taitt, L. Shriver-Lake, K. Sapsford, Y. Shubin, and J. Golden, Array biosensor for detection of toxins, Anal Bioanal Chem 377(3) (2003), 469-477 [Online]. http://dx.doi.org/10.1007/s00216-003-1992-0.
8. D. V. Lim, J. M. Simpson, E. A. Kearns, and M. F. Kramer, Current and developing technologies for monitoring agents of bioterrorism and biowarfare, Clin Microbiol Rev 18(4) (2005), 583-607.
9. L. Manning, R. Baines, and S. Chadd, Deliberate contamination of the food supply chain, Brit Food J 107(4) (2005), 225-245 [Online].
10. D. A. Relman, E. Choffnes, and S. M. Lemon, In search of biosecurity, Science 311(5769) (2006), 1835 [Online].
11. B. Rajwa, M. Venkatapathi, K. Ragheb, P. P. Banada, E. D. Hirleman, T. Lary, and J. P. Robinson, Automated classification of bacterial particles in flow by multiangle scatter measurement and support vector machine classifier, Cytometry. Part A: J Int Soc Anal Cytol 73(4) (2008), 369-379. PMID: 18163466 [Online].
12. T. Lane, and C. E. Brodley, Temporal sequence learning and data reduction for anomaly detection, ACM Trans Inform Syst Security 2 (1998), 150-158.
13. E. Eskin, A. Arnold, M. Prerau, L. Portnoy, and S. Stolfo, A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data, In Applications of Data Mining in Computer Security, D. Barbará, and S. Jajodia, eds. Kluwer, 2002.
14. D. Pelleg, and A. Moore, Active learning for anomaly and rare-category detection, Advances in Neural Information Processing Systems, Vol. 18, MIT Press, December 2004, 1073-1080.
15. J. Theiler, and D. M. Cai, Resampling approach for anomaly detection in multispectral images, In Proc. SPIE, 2003. 230-240.
16. B. Schölkopf, R. Williamson, A. Smola, J. Shawe-Taylor, and J. Platt, Support vector method for novelty detection, Adv Neural Inform Process Syst 12 (2000), 1443-1471.
17. J. Muoz-Mar, L. Bruzzone, and G. Camps-Valls, A support vector domain description approach to supervised classification of remote sensing images, IEEE Trans Geosci Remote Sens 45(8) (2008), 2683-2692.
18. E. J. Spinosa, and A. C. Carvalho, Support vector machines for novel class detection in bioinformatics, Genet Mol Res 4(3) (2005), 608-615.
19. D. M. J. Tax, and R. P. W. Duin, Support vector domain description, Pattern Recogn Lett 20(11-13) (1999), 1191-1199.
20. B. Schölkopf, J. C. Platt, J. C. Shawe-Taylor, A. J. Smola, and R. C. Williamson, Estimating the support of a high-dimensional distribution, Neural Comput 13(7) (2001), 1443-1471.
21. M. Dundar, D. Hirleman, A. K. Bhunia, J. P. Robinson, and B. Rajwa, Learning with a nonexhaustive training dataset. A case study: detection of bacteria cultures using optical-scattering technology, In Proceedings of the Fifteenth Annual SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, 2009.
22. K. Fukunaga, Introduction to Statistical Pattern Recognition, San Diego, CA, Academic Press, 1990.
23. R. E. Bellman, Dynamic Programming, New Jersey, Princeton University Press, 1957.
24. J. H. Friedman, Regularized discriminant analysis, J Am Stat Assoc 84(405) (1989), 165-175.
25. T. Greene, and W. Rayens, Partially pooled covariance matrix estimation in discriminant analysis, Commun Stat Theory Meth 18(10) (1989), 3679-3702.
26. T. W. Anderson, An Introduction to Multivariate Statistical Analysis (3rd ed.), New York, Wiley-Interscience, 2003.
27. G. McLachlan, and D. Peel, Finite Mixture Models, New York, NY, John Wiley & Sons, 2000.
28. A. Dempster, N. Laird, and D. Rubin, Maximum likelihood from incomplete data via the em algorithm, J Roy Stat Soc 39(1) (1977), 1-38.
29. P. P. Banada, K. Huff, E. Bae, et al., Label-free detection of multiple bacterial pathogens using light-scattering sensor, "Biosens Bioelectron 24(6) (2009), 1685-92. PMID: 18945607 [Online].
30. P. W. Frey, and D. J. Slate, Letter recognition using holland-style adaptive classifiers, Mach Learn 6(2) (1991), 161-182.
31. R. E. Kass, and A. E. Raftery, Bayes factors, J Am Stat Assoc 90(430) (1995), 773-795.