Inducing inference rules for the classification of bovine mastitis

Computers and Electronics in Agriculture

Volumn 23, Issue 1, 1999, Pages 27-42

  Kim, Taioun ^a   Heald, C William ^b  

^a Kyungsung University   (South Korea)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Classification; Inference rules; Machine learning; Mastitis

Indexed keywords

EID: 0032846871     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0168-1699(99)00003-4     Document Type: Article

References (27)

1. Berning L.M., Shook G.E. Prediction of mastitis using milk somatic cell count, N-Acetyl-b-D-glucosaminidase, and lactose. J. Dairy Sci. 75:1992;1840-1848.
2. Branting L.K., Hastings J.D., Lockwood J.A. Integrating cases and models for prediction in biological systems. Artif. Intell. Appl. 11(1):1997;29-48.
3. Carbonell J.G. Introduction: paradigms for machine learning. Artif. Intell. 40:1989;1-9.
4. Dybowski R., Gant V. Artificial neural networks in pathology and medical laboratories. The Lancet. 346(4):1995;1203-1207.
5. Gerevini A., Perini A., Forti D., Ioriatti C., Mattedi L. POMI: an expert system for integrated pest management of apple orchards. Artif. Intell. Appl. 6(3):1992;51-62.
6. Gu Y., McNicol J.W., Peiris D.R., Crawford J.W., Marshall B., Jefferies R.A. A belief network-based system for prediction future crop production. Artif. Intell. Appl. 10(1):1996;13-24.
7. Guan B.T., Gertner G.Z. Modeling individual tree survival probability with a random optimization procedure: an artificial neural network approach. Artif. Intell. Appl. 9(2):1995;39-52.
8. Harmon, R.J., Eberhart, R.J., Jasper, E.D., Langlois, B.E., Wilson, R.A., 1990. Microbiological procedures for the diagnosis of bovine udder infection. National Mastitis Council, Arlington, p. 34.
9. Heald, C.W., Kim, T., Cooper, J.B., Wolfgang, D.R., Sischo, W.M., 1999. A computerized mastitis decision aid for farm production records: a neural network approach. Working paper. The Pennsylvania State University, Department of Dairy and Animal Sciences, PA.
10. Hogeveen, H., Noordhuizen-Stassen, E.N., Thysen, I., Van Werven, T., Lam, T.J.G.M., 1995. Automated pathogen diagnosis at low somatic cell count farms. In: Saren, A., Soback, S. (Eds), Proceedings of the 3rd International Mastitis Seminar, Vol. 1. Tel Aviv, Israel, S2 pp. 47-51.
11. Hunt E.B., Marin J., Stone P.T. Experiments in Induction. 1966;45-69 Academic Press, New York.
12. Jones G.F., Ward G.E. Evaluation of a scheme for predicting the gram-staining reaction of organisms causing bovine mastitis. J. Am. Vet. Med. Assoc. 196(4):1990;597-599.
13. Lee J., Song Y., Kwon S., Kim W., Kim M. Development of Expert System using UNIK. 1996;BupYoungSa, Seoul.
14. McQueen R.J., Garner S.R., Nevill-Manning C.G., Whitten I.W. Applying machine learning to agricultural data. Comput. Electron. Agric. 12:1995;275-293.
15. Michalski R.S., Chilausky R.L. Learning by being told and from examples: an experimental comparison of the two methods of knowledge acquisition in the context of developing an expert system for soybean disease diagnosis. Int. J. Policy Anal. Info. Syst. 4:1980;125-161.
16. Michalski, R.S., 1986. A theory and methodology of inductive learning. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (Eds), Machine Learning: An Artificial Intelligence Approach. Morgan Kaufmann, Palo Alto, CA, pp. 83-129.
17. Mitchell R.S., Sherlock R.A., Smith L.A. An investigation into the use of machine learning for determining oestrus in cows. Comput. Electron. Agric. 15:1996;195-213.
18. Morin D.E., Constable P.D., McCoy G.C. Use of clinical parameters for differentiation of gram-positive and gram-negative mastitis in dairy cows vaccinated against lipopolysaccharide core antigens. J. Am. Vet. Med. Assoc. 212(9):1998;1423-1431.
19. Mulder J.A., Corns I.G.W. NAIA: a decision support system for predicting ecosystems from existing land resource data. Artif. Intell. Appl. 9(1):1995;49-60.
20. Current concepts of bovine mastitis. Fourth edition:1996;National Mastitis Council, Arlington, VA. pp. 1-3.
21. National Mastitis Council, 1998. The value and use of dairy herd improvement somatic cell count, NMC fact sheet. National Mastitis Council, Madison, WI.
22. Ott, S.L., 1999. Costs of herd-level production losses associated with subclinical mastitis in U.S. dairy cows. Proceedings of 38th Annual Meeting of the National Mastitis Council. Arlington, VA, pp.152-153.
23. Quinlan J.R. Induction of decision trees. Mach. Learn. 1:1986;81-106.
24. Quinlan J.R. Learning logical relations from definitions. Mach. Learn. 5:1990;239-266.
25. Quinlan J.R. C4.5: Programs for Machine Learning. 1992;1-16 Morgan Kaufmann, Los Altos, CA.
26. White M.E., Glickman L.T., Barnes-Pallesen F.D., Pearson E.G., Montgomery M.E., Armstrong D., Wickenden R.P., Hickey G. Discriminant analysis of the clinical indicants for bovine coliform mastitis. Cornell Vet. 76(4):1986;335-341.
27. White M.E., Glickman L.T., Barnes-Pallesen F.D., Stem E.S. III, Dinsmore P., Powers M.S., Powers P., Smith M.C., Montgomery M.E., Jasko D. Accuracy of a discriminant ananlysis model for prediction of coliform mastitis in dairy cows and a comparison with clinical prediction. Cornell Vet. 76(4):1986;342-347.