Detecting noisy instances with the ensemble filter: A study in software quality estimation

International Journal of Software Engineering and Knowledge Engineering

Volumn 16, Issue 1, 2006, Pages 53-76

  Khoshgoptaar, Taghi M ^a   Joshi, Vedang ^a   Seliya, Naeem ^b  

^a Florida Atlantic Univ   (United States)

^b UNIVERSITY OF MICHIGAN DEARBORN   (United States)

Author keywords

Ensemble filter; Noise detection; Software metrics; Software quality

Indexed keywords

ACOUSTIC NOISE; ESTIMATION; MAGNETIC FILTERS; METRIC SYSTEM;

ENSEMBLE FILTERS; NOISE DETECTION; SOFTWARE METRICS; SOFTWARE QUALITY;

COMPUTER SOFTWARE;

EID: 33644969450     PISSN: 02181940     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218194006002677     Document Type: Conference Paper

References (37)

1. D. Gamberger, N. Lavrač and C. Grošelj, Experiments with noise filtering in a medical domain, in Proc. 16th Int. Conf. on Machine Learning (Morgan Kaufmann, CA, 1999), pp. 143-151.
2. D. Gamberger and N. Lavrac, Conditions for Occam's razor applicability and noise elimination, in European Conf. on Machine Learning, 1997, pp. 108-123.
3. C. M. Teng, Correcting noisy data, in Proc. 16th Int. Conf. on Machine Learning (Washington, DC, 1999), pp. 239-248.
4. C. E. Brodley and M. A. Friedl, Identifying mislabeled training data, J. Artificial Intelligence Research 11 (1999) 131-167.
5. X. Zhu, X. Wu and Q. Chen, Eliminating class noise in large datasets, in Proc. 20th Int. Conf. on Machine Learning (Washington, DC, 2003).
6. I. H. Whitten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with JAVA Implementations (Morgan Kaufmann, CA, 2000).
7. T. M. Khoshgoftaar and N. Seliya, Analogy-based practical classification rules for software quality estimation, Empirical Software Engineering Journal 8 (2003) 325-350.
8. T. M. Khoshgoftaar, X. Yuan and E. B. Allen, Balancing misclassification rates in classification tree models of software quality, Empirical Software Engineering Journal 5 (2000) 313-330.
9. T. M. Khoshgoftaar and E. B. Allen, Logistic regression modeling of software quality, Int. J. Reliability, Quality and Safety Engineering 6 (1999) 303-317.
10. N. F. Schneidewind, Investigation of logistic regression as a discriminant of software quality, in Proc. 7th Int. Software Metrics Symposium, London, UK, 2001, IEEE Computer Society, pp. 328-337.
11. T. M. Khoshgoftaar and N. Seliya, Comparative assessment of software quality classification techniques: An empirical case study, Empirical Software Engineering Journal 9(3) (2004) 229-257.
12. Y. Liu and T. M. Khoshgoftaar, Genetic programming model for software quality prediction, in Proc. 6th Int. High Assurance Systems Engineering Symposium, Boca Raton, FL, 2001, IEEE Computer Society, pp. 127-136.
13. C. T. Lin and C. S. G. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems (Prentice Hall, NJ, 1996).
14. T. M. Khoshgoftaar and N. Seliya, Fault prediction modeling for software quality estimation: Comparing commonly used techniques, Empirical Software Engineering Journal 8 (2003) 255-283.
15. T. M. Khoshgoftaar, N. Seliya and K. Gao, Rule-based noise detection for software measurement data, in Proc. IEEE Int. Conf. on Information Reuse and Integmtion, Las Vegas, NV, 2004, IEEE Systems, Man and Cybernetics Society, pp. 302-307.
16. M. Mao, Software quality classification using rule-based modeling, Master's thesis, Florida, Atlantic University (May 2002). Advised by Taghi M. Khoshgoftaar.
17. J. Komorowski, L. Polkowski and A. Skowron, Rough Set: A Tutorial (Springer-Verlag, 1998).
18. T. M. Khoshgoftaar and L. A. Bullard, Software quality estimation with rough sets theory, Tech. Rep. TR.-CSE-03-20, Florida, Atlantic University (2003).
19. J. Friedman, J. Stochastic, T. Hastie and R., Tibshirani, Additive logistic regression: A statistical view of boosting (1999).
20. L. Breiman, Bagging predictors, Machine Learning 24(2) (1996) 123-140.
21. R. Kohavi, The power of decision tables, in Proc. European Conf. on Machine Learning, eds. N. Lavrač and S. Wrobel Lecture Notes in Artificial Intelligence (Springer-Verlag, 1995), pp. 174-189.
22. Y. Freund and L. Mason, The alternating decision tree learning algorithm, in Proc. 16th Int. Conf. on Machine Learning (Morgan Kaufmann, 1999), pp. 124-133.
23. J. C. Platt, Sequential minimal optimization: A fast algorithm for training support vector machines, Tech. Rep. 98-14, Microsoft Research (Redmond, WA, 1998).
24. E. Frank and I. H. Witten, Generating accurate rule sets without global optimization, in Proc. 15th Int. Conf. on Machine Learning (Morgan Kaufmann, 1998), pp. 144-151.
25. J. R. Quinlan, C4.5: Programs for Machine Learning (Morgan Kaufmann, 1993).
26. W. W. Cohen, Fast effective rule induction, in Proc. 12th Int. Conf. on Machine Learning, eds. A. Prieditis and S. Russell (Morgan Kaufmann, 1995), pp. 115-123.
27. R. C. Holte, Very simple classification rules perform well on most commonly used datasets, Machine Learning 11 (1993) 63-91.
28. P. Compton and R. Jansen, A philosophical basis for knowledge acquisition, Knowledge Acquisition 2(3) (1990) 241-258.
29. E. Frank, L. Trigg, G. Holmes and I. H. Witten, Naive bayes for regression, Machine Learning 41(1) (2000) 5-25.
30. J. Peng, F. Ertl, S. Bhagotra, A. Mosam, N. Vijayaratnam and I. Kanwal, Classification of U.S. census data, Data Mining Project CS4TF3, http://www.census.gov.
31. C. G. Atkeson, A. W. Moore and S. Schaal, Locally weighted learning, Artificial Intelligence Review 11(1-5) (1997) 11-73.
32. A. Danyluk and F. Provost, Small disjuncts in action: Learning to diagnose errors in the telephone network local loop, in Proc. 10th Int. Conf. on Machine Learning (Morgan Kaufmann, 1993), pp. 81-88.
33. R. A. Johnson and D. W. Wichern Applied Multivariate Statistical Analysis, 2nd edn. (Prentice Hall, NJ, 1992).
34. V. R. Basili, L. C. Briand and W. L. Melo, A validation of object-oriented design metrics as quality indicators, IEEE Trans. on Software Engineering 22 (1996) 751-761.
35. K. E. Imam, S. Benlarbi, N. Goel and S. N. Rai, Comparing case-based reasoning classifiers for predicting high-risk software components, J. Systems and Software 55(3) (2001) 301-320.
36. Y. Ping, T. Systa and H. Muller, Predicting fault-proneness using OO metrics: An industrial case study, in Proc. 6th European Conf. on Software Maintenance and Reengineering, eds. T. Gyimothy and F. B. Abreu (Budapest, Hungary, 2002), pp. 99-107.
37. W. J. Conover, Practical Nonparwnetric Statistics (John Wiley, New York, 1971).