Machine learning methods for predicting failures in hard drives: A multiple-instance application

Journal of Machine Learning Research

Volumn 6, Issue , 2005, Pages

  Murray, Joseph F ^a   Hughes, Gordon F ^b   Kreutz Delgado, Kenneth ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Exact nonparametric statistics; Hard drive failure prediction; Multiple instance naive Bayes; Rank sum test; Support vector machines (SVM)

Indexed keywords

ALGORITHMS; HARD DISK STORAGE; PROBABILITY; PROBLEM SOLVING; STATISTICAL TESTS; TIME SERIES ANALYSIS;

EXACT NONPARAMETRIC STATISTICS; HARD DRIVE FAILURE PREDICTION; MULTIPLE INSTANCE NAIVE-BAYES; RANK-SUM TEST; SUPPORT VECTOR MACHINES (SVM);

LEARNING SYSTEMS;

EID: 21844431631     PISSN: 15337928     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (42)

1. Stuart Andrews, Ioannis Tsochantaridis, and Thomas Hofmann. Support vector machines for multiple-instance learning. In Advances in Neural Information Processing Systems 15 (NIPS*2002), pages 1-8, Cambridge, MA, 2003. MIT Press.
2. Julius S. Bendat and Allan G. Piersol. Random Data. Wiley, New York, 3rd edition, 2000.
3. P. J. Bickel and K. A. Doksum. Mathematical Statistics. Holden-Day, San Francisco, 1977.
4. P. D. Bridge and S. S. Sawilowsky. Increasing physicians' awareness of the impact of statistics on research outcomes: Comparative power of the t-test and Wilcoxon rank-sum test in small samples applied research. Journal Of Clinical Epidemiology, 52(3):229-235, March 1999.
5. Edgar Brunner, Ullrich Munzel, and Madan L. Puri. The multivariate nonparametric Behrens-Fisher problem. Journal of Statistical Planning and Inference, 108:37-53, 2002.
6. Christopher J. C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2:121-167, 1998.
7. J. Catlett. On changing continuous attributes into ordered discrete attributes. In Y. Kodratoff, editor, Proceedings of the European Working Session on Learning, pages 164-178, Berlin, 1991. Springer-Verlag.
8. P. Cheeseman and J. Stutz. Advances in Knowledge Discovery and Data Mining, chapter Bayesian Classification (AutoClass), pages 158-180. AAAI Press, Menlo Park, CA, 1995.
9. Vladimir Cherkassky and Filip Mulier. Learning from Data: Concepts, Theory, and Methods. Wiley, New York, 1998.
10. Thomas G. Dietterich, Richard H. Lathrop, and Tomas Lozano-Perez. Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence, 89:31-71, 1997.
11. E. Jacquelin Dietz and Timothy J. Killeen. A nonparametric multivariate test for monotone trend with pharmaceutical applications. Journal of the American Statistical Association, 76(373): 169-174, March 1981.
12. Pedro Domingos and Michael Pazzani. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29:103-130, 1997.
13. J. Dougherty, R. Kohavi, and M. Sahami. Supervised and unsupervised discretization of continuous features. In Proceedings of the 12th International Conference on Machine Learning, pages 194-202. Morgan Kaufmann, 1995.
14. J. D. Emerson and L. E. Moses. A note on the Wilcoxon-Mann-Whitney test for 2 × k ordered tables. Biometrics, 41:303-309, March 1985.
15. Eibe Frank and Ian H. Witten. Making better use of global discretization. In I. Bratko and S. Dzeroski, editors, Proceedings of the Sixteenth International Conference on Machine Learning, Bled, Slovenia, pages 115-123, San Francisco, CA, 1999. Morgan Kaufmann Publishers.
16. Greg Hamerly and Charles Elkan. Bayesian approaches to failure prediction for disk drives. In Eighteenth International Conference on Machine Learning, pages 1-9, 2001.
17. T. P. Hettmansperger. Statistical Inference Based on Ranks. Wiley, New York, 1984.
18. Gordon F. Hughes, Joseph F. Murray, Kenneth Kreutz-Delgado, and Charles Elkan. Improved diskdrive failure warnings. IEEE Transactions on Reliability, 51(3):350-357, September 2002.
19. Maurice G. Kendall. The Advanced Theory of Statistics, volume 1. Hafner, New York, 1969.
20. J. H. Klotz. The Wilcoxon, ties, and the computer. Journal of the American Statistical Association, 61(315):772-787, September 1966.
21. S. Y. Lehman. Exact and approximate distributions for the Wilcoxon statistic with ties. Journal of the American Statistical Association, 56(294) :293-298, 1961.
22. E. L. Lehmann and H. J. M. D'Abrera. Nonparametrics: Statistical Methods Based on Ranks. Prentice Hall, Upper Saddle River, NJ, 1998.
23. Faming Liang. An effective Bayesian neural network classifier with a comparison study to support vector machine. Neural Computation, 15:1959-1989, 2003.
24. Henry B. Mann. Nonparametric tests against trend. Econometrica, 13(3):245-259, 1945.
25. Henry B. Mann and D. R. Whitney. On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, 19:50-60, 1947.
26. C. R. Mehta, N. R. Patel, and P. Senchaudhuri. Importance sampling for estimating exact probabilities in permutational inference. Journal of the American Statistical Association, 83(404): 999-1005, December 1988a.
27. C. R. Mehta, N. R. Patel, and L. J. Wei. Constructing exact significance tests with restricted randomization rules. Biometrika, 75:295-302, 1988b.
28. Joseph F. Murray, Gordon F. Hughes, and Kenneth Kreutz-Delgado. Hard drive failure prediction using non-parametric statistical methods. In Proceedings of the International Conference on Artificial Neural Networks ICANN 2003, Istanbul, Turkey, June 2003.
29. A. Y. Ng and M. I. Jordan. On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes. In Advances in Neural Information Processing Systems (NIPS), volume 14, 2002.
30. Alon Orlitsky, Narayana P. Santhanam, and Junan Zhang. Always Good Turing: Asymptotically optimal probability estimation. Science, 302:427-431, October 2003.
31. M. Pagano and D. Tritchler. On obtaining permutation distributions in polynomial time. Journal of the American Statistical Association, 78:435-441, 1983.
32. B. E. Preusser and G. L. Hadley. Motor current signature analysis as a predictive maintenance tool. In Proceedings of the American Power Conference, Illinois Institute of Technology, pages 286-291, April 1991.
33. K. Rothman and S. Greenland. Modern Epidemiology. Lippencott-Raven, Philadelphia, 2nd ed. edition, 2000.
34. Stefan Ruping. mySVM manual. Technical report, University of Dortmund, CS Department, AI Unit, October 2000.
35. P. T. Theodossiou. Predicting shifts in the mean of a multivariate time series process: an application in predicting business failures. Journal of the American Statistical Association, 88(422):441-449, 1993.
36. Michael E. Tipping. Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 1:211-244, 2001.
37. V. Vapnik. The Nature of Statistical Learning Theory. Springer Verlag, New York, 1995.
38. Pascal Vincent and Yoshua Bengio. Kernal matching pursuit. Machine Learning, 48:165-187, 2002.
39. Jun Wang and Jean-Daniel Zucker. Solving multiple-instance problem: A lazy learning approach. In Pat Langley, editor, Proceedings of the Seventeenth International Conference on Machine Learning, pages 1119-1125, Stanford, CA, 2000. Morgan Kaufmann.
40. G. M. Weiss and H. Hirsh. Learning to predict rare events in event sequences. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining. AAAI Press, August 1998.
41. F. Wilcoxon. Individual comparisons by ranking methods. Biometrics Bulletin, 1(6):80-83, December 1945.
42. Xin Xu. Statistical learning in multiple instance problems. Master's thesis, University of Waikato, Hamilton, New Zealand, June 2003.