Identifying Mislabeled Training Data

Journal of Artificial Intelligence Research

Volumn 11, Issue , 1999, Pages 131-167

  Brodley, Carla E ^a   Friedl, Mark A ^b  

^a PURDUE UNIVERSITY   (United States)

^b BOSTON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0000046054     PISSN: 10769757     EISSN: None     Source Type: Journal    
DOI: 10.1613/jair.606     Document Type: Article

References (56)

1. Aha, D., Kibler, D., & Albert, M. (1991). Instance-based learning algorithms. Machine Learning, 6(1), 37-66.
2. Angluin, D., & Laird, P. (1988). Learning from noisy examples. Machine Learning, 2(4), 343-370.
3. Bain, M., & Muggleton, S. (1991). Non-montonic learning. Machine Intelligence, 12, 105-120.
4. Benediktsson, J., & Swain, P. (1992). Consensus theoretic classification methods. IEEE Transactions on Systems, Man, and Cybernetics, 22(4), 668-704.
5. Brodley, C. E. (1995). Recursive automatic bias selection for classifier construction. Machine Learning, 20, 63-94.
6. Brodley, C. E., & Friedl, M. A. (1996a). Identifying and eliminating mislabeled training instances. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pp. 799-805 Portland, OR. AAAI Press.
7. Brodley, C. E., & Friedl, M. A. (1996b). Improving automated land cover mapping by identifying and eliminating mislabeled observations from training data. In Proceedings of the International Geoscience and Remote Sensing Symposium, Vol. II, pp. 1382-1384 Lincoln, NB.
8. Brodley, C. E., & Utgoff, P. E. (1995). Multivariate decision trees. Machine Learning, 19, 45-77.
9. Cardie, C. (1993). Using decision trees to improve case-based learning. In Machine Learning: Proceedings of the Tenth International Conference, pp. 25-32 Amherst, MA. Morgan Kaufmann.
10. Danyluk, A., & Provost, F. (1993). Small disjuncts in action: Learning to diagnose errors in the telephone network local loop. In Machine Learning: Proceedings of the Tenth International Conference, pp. 81-88 Amherst, MA. Morgan Kaufmann.
11. Decatur, S. (1996). Learning in hybrid noise environments using statistical queries. In Fisher, D., & Lenz, H. (Eds.), Learning from Data: Artificial Intelligence and Statistics V, pp. 175-185. Springer Verlag, New York.
12. Decatur, S. (1997). Pac learning with constant-partition classification noise and applications to decision tree induction. In Machine Learning: Proceedings of the Fourteenth International Conference, pp. 83-91 Nashville, TN. Morgan Kaufman.
13. Defries, R. S., & Townshend, J. R. G. (1994). NDVI-derived land cover classifications at a global scale. International Journal of Remote Sensing, 15(17), 3567-3586.
14. Dietterich, T. G. (1998). Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10(7), 1895-1924.
15. Dietterich, T. G., & Bakiri, G. (1995). Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2, 263-286.
16. Dowe, D. L., & Krusel, N. (1993a). A decision tree model of bushfire activity. In Proceedings of the Sixth Australian Joint Conference on Artificial Intelligence, pp. 287-292. World Scientific.
17. Dowe, D. L., & Krusel, N. (1993b). A decision tree model of bushfire activity. Tech. rep. Technical report 93/190, Monash University, Melbourne, Australia. This technical report corrects the article appearing in the Sixth Australian Joint Conference on Artificial Intelligence.
18. Dowe, D. L., & Krusel, N. (1994). Decision tree models of bushfire activity. AI Applications, 5(3), 71-72.
19. Draper, B. (1998). Personal communication..
20. Draper, B., Collins, R., Brolio, J., Hanson, A., & Riseman, E. (1989). The Schema System. International Journal of Computer Vision, 2, 209-250.
21. Duda, R. O., & Hart, P. E. (1973). Pattern Classification and Scene Analysis. John Wiley & Sons, NY.
22. Fayyad, U. M., & Irani, K. B. (1992). On the handling of continuous-valued attributes in decision tree generation. Machine Learning, 8(1), 87-102.
23. Gamberger, D., Lavrač, N., & Džeroski, S. (1996). Noise elimination in inductive concept learning: A case study in medical diagnosis. In Seventh International Workshop on Algorithmic Learning Theory, pp. 199-212. Springer.
24. Guyon, I., Matic, N., & Vapnik, V. (1996). Discovering informative patterns and data cleaning. In Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurasamy, R. (Eds.), Advances in Knowledge Discovery and Data Mining, pp. 181-203. AAAI/MIT Press.
25. Hansen, L. K., & Salamon, P. (1990). Neural network ensembles. IEEE Transactions of Pattern Analysis and Machine Intelligence, 12(10), 993-1001.
26. John, G. H. (1995). Robust decision trees: Removing outliers from data. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining, pp. 174-179 Montreal, Quebec. AAAI Press.
27. Krusel, N., & Dowe, D. L. (1993). Predicting bushfire activity in the Mallee region of the northwest Victoria using a decision tree model. In Proceedings of the Institute of Australian Geographers Conference Melbourne, Australia.
28. Lewis, D., & Catlett, J. (1994). Heterogeneous uncertainty sampling for supervised learning. In Machine Learning: Proceedings of the Eleventh International Conference, pp. 148-156 New Brunswick, NJ. Morgan Kaufmann.
29. Los, S. O., Justice, C. O., & Tucker, C. J. (1994). A global 1 degree by 1 degree NDVI data set for climate studies derived from the Gimms continental NDVI data. International Journal of Remote Sensing, 15(17), 3493-3519.
30. Matthews, E. (1983). Global vegetation and land use: New high resolution data bases for climate studies. Journal of Climate and Applied Meteorology, 22, 474-487.
31. Michie, D. (1989). Problems of computer-aided concept formation.. In Quinlan, J. R. (Ed.), Applications of Expert Systems, Vol. 2. Addison-Wesley, Wokingham, UK.
32. Nilsson, N. J. (1965). Learning machines. McGraw-Hill, New York.
33. Oates, T., & Jensen, D. (1997). The effects of training set size on decision tree complexity. In Machine Learning: Proceedings of the Fourteenth International Conference, pp. 254-262 Nashville, TN. Morgan Kaufman.
34. Oka, N., & Yoshida, K. (1993). Learning regular and irregular examples separately. In Proceedings of the 1993 IEEE International Joint Conference on Neural Networks, pp. 171-174. IEEE Press.
35. Oka, N., & Yoshida, K. (1996). A noise-tolerant hybrid model of a global and a local learning model. In Proceedings of the AAAI-96 Workshop: Integrating Mutiple Learned Modesl for Improving and Scaling Machine Learning Algorithms, pp. 95-100. AAAI Press.
36. Olson, J., Watts, J., & Allison, L. (1983). Carbon in live vegetation of major world ecosystems. Tech. rep. Technical Report W-7405-ENG-26, US Department of Energy, Oak Ridge National Laboratory.
37. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81-106.
38. Quinlan, J. R. (1993). C4.5: Programs for machine learning. Morgan Kaufmann, San Mateo, CA.
39. Quinlan, J. R. (1996). Bagging, boosting and C4.5. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pp. 725-730 Portland, OR. AAAI Press.
40. Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5, 197-227.
41. Skalak, D. (1994). Prototype and feature selection by sampling and random mutation hill climbing algorithms. In Machine Learning: Proceedings of the Eleventh International Conference, pp. 293-301 New Brunswick, NJ. Morgan Kaufmann.
42. Skalak, D., & Rissland, E. (1990). Inductive learning in a mixed paradigm setting. In Proceedings of the Eighth National Conference on Artificial Intelligence, pp. 840-847 Boston, MA. Morgan Kaufmann.
43. Sloan, R. (1988). Types of noise in data for concept learning. In Proceedings of the First Workshop on Computational Learning Theory, pp. 91-96 Cambridge, MA. ACM Press.
44. Sloan, R. (1995). Four types of noise in data for PAC learning. Information Processing Letters, 54(3), 157-162.
45. Smyth, P. (1996). Bounds on the mean classification error rate of multiple experts. Pattern Recognition Letters, 17, 1253-1327.
46. Srinivasan, A., Muggleton, S., & Bain, M. (1992). Distinguishing exceptions from noise in non-monotonic learning. In Proceedings of the Second Inductive Logic Programming Workshop, pp. 97-107 Tokyo, Japan. ICOT (Technical report TM-1182).
47. Tomek, I. (1976). An experiment with edited nearest-neighbor rule. IEEE Transactions on Systems, Man and Cybernetics, 6(6), 448-452.
48. Weisberg, S. (1985). Applied linear regression. John Wiley & Sons.
49. Wilson, D. (1972). Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans, on Systems, Alan and Cybernetics, 2, 408-421.
50. Wilson, D. R. (1997). Advances in instance-based learning algorithms. Ph.D. thesis, Brigham Young University, Provo, UT.
51. Wilson, D. R., & Martinez, T. R. (1997). Instance pruning techniques. In Machine Learning: Proceedings of the Fourteenth International Conference, pp. 404-411 Nashville, TN. Morgan Kaufman.
52. Wilson, D. R., & Martinez, T. R. (1999). Reduction techniques for exemplar-based learning algorithms. Machine Learning, in press.
53. Wilson, M. F., & Henderson-Sellers, A. (1985). A global archive of land cover and soils data for use in general circulation models. Journal of Climatology, 5, 119-143.
54. Winston, P. H. (1975). Learning structural descriptions from examples. In Winston, P. H. (Ed.), The Psychology of Computer Vision. McGraw-Hill, New York.
55. Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5, 241-259.
56. Zhao, Q., & Nishida, T. (1995). Using qualitative hypotheses to identify inaccurate data. Journal of Artificial Intelligence Research, 3, 119-145.