Attribute selection using rough sets in software quality classification

International Journal of Reliability, Quality and Safety Engineering

Volumn 16, Issue 1, 2009, Pages 73-89

  Khoshgoftaar, Taghi M ^a   Bullard, Lofton A ^a   Gao, Kehan ^b  

^a Florida Atlantic Univ   (United States)

^b EASTERN CONNECTICUT STATE UNIVERSITY   (United States)

Author keywords

Attribute selection; Balancing misclassification; Reducts; Rough set; Software metrics based classification model

Indexed keywords

ATTRIBUTE SELECTION; CASE-BASED REASONINGS; CLASSICAL SET THEORIES; CLASSIFICATION PERFORMANCE; DATA SETS; DISCERNIBILITY; EMPIRICAL STUDIES; QUALITY OF SOFTWARES; REDUCTS; ROUGH SET; SMALL GROUPS; SOFTWARE DEVELOPERS; SOFTWARE METRICS-BASED CLASSIFICATION MODEL; SOFTWARE QUALITIES; SOFTWARE SYSTEMS; TRAINING DATA SETS;

CASE BASED REASONING; COMPUTER SOFTWARE SELECTION AND EVALUATION; FUZZY SETS;

ROUGH SET THEORY;

EID: 65249085590     PISSN: 02185393     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218539309003307     Document Type: Article

References (30)

1. Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning About Data, Kluwer Academic (1992).
2. L. A. Bullard, T. M. Khoshgoftaar and K. Gao, An application of a rule-based model in software quality classification, in Proceedings of the 6th IEEE International Conference on Machine Learning and Applications (ICMLA'07), Cincinnati, Ohio, USA: IEEE Computer Society, (December 13-15 2007), pp. 204-210.
3. M. Salamo and E. Golobardes, Analyzing rough sets weighting methods for case-based reasoning systems, in Artificial Intelligence: Latin American Magazine of Artificial Intelligence 15 (2002) 34-43.
4. D. Slezak and J. Wroblewski, Covering with reducts a fast algorithm for rule generation, in Proceedings: RSCTC Vol. 1424, Springer Verlag, (1998), pp. 402-407.
5. T. M. Khoshgoftaar, E. B. Allen and J. Deng, Using regression trees to classify faultprone software modules, IEEE Transactions on Reliability 51(4) (2002) 455-462.
6. C. Ebert, Classification techniques for metric-based software development, Software Quality Journal 5 (1996), 255-272.
7. Z. Xu, N. Seliya and W. Wu, An adaptive neural network with dynamic structure for software defect prediction, in Proceedings of the Twentieth International Conference on Software Engineering and Knowledge Engineering (SEKE'2008), San Francisco, CA, USA (2008), pp. 79-84.
8. J. C. Munson and T. M. Khoshgoftaar, The detection of fault-prone programs, IEEE Transactions on Software Engineering 18 (1992) 423-433.
9. T. M. Khoshgoftaar, N. Seliya and K. Gao, Assessment of a new three-group software quality classification technique: An empirical case study, Empirical Software Engineering 10(2) (2005) 183-218.
10. Y. Freund and R. E. Schapire, Experiments with a new boosting algorithm, in Proc. 13th International Conference on Machine Learning. Morgan Kaufmann, (1996), pp. 148-156.
11. L. Briand, V. Basili and C. Hetmanski, Developing interpretable models with optimized set reduction for identifying high-risk software components, IEEE Transactions on Software Engineering 19 (1993) 1028-1044.
12. T. M. Khoshgoftaar and N. Seliya, Analogy-based practical classification rules for software quality estimation, Empirical Software Engineering Journal 8(4) (2003) 325-350.
13. M. A. Hall, Correlation-based feature selection for discrete and numeric class machine learning, in Proc. 17th International Conf. on Machine Learning. Morgan Kaufmann, San Francisco, CA, (2000), pp. 359-366.
14. C. Kirsopp, M. J. Shepperd and J. Hart, Search heuristics, case-based reasoning and software project effort prediction, in Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kauffman Publisher Inc., San Francisco, CA (USA) (2002), pp. 1367-1374.
15. G. Bontempi, Structural feature selection for wrapper methods, in Proceedings of ESANN'2005, European Symposium on Artificial Neural Networks, Bruges, Belgium, (April 27-29 2005), pp. 405-410.
16. H. B. Borges and J. C. Nievola, Attribute selection methods comparison for classification of diffuse large B-cell lymphoma, in Proceedings of World Academy of Science, Engineering and Technology, Vol. 8 (October 2005), pp. 193-197.
17. D.W. Aha and R. L. Bankert, A comparative evaluation of sequential feature selection algorithms, in Proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, Fort Lauderdale, Florida, USA: IEEE Computer Society (January 1995).
18. D. B. Skalak, Prototype and feature selection by sampling and random mutation hill climbing algorithms, in International Conference on Machine Learning (1994), pp. 293-301.
19. G. Brassard and P. Bratley, Fundamental of Algorithmics, Prentice-Hall, (1996).
20. J. Wroblewski, Finding minimal reducts using genetic algorithms, in Proceedings of the International Workshop on Rough Sets Soft Computing at Second Annual Joint Conference on Information Sciences (1995), pp. 186-189.
21. -, Genetic algorithms in decomposition and classification problems, in Rough Sets in Knowledge Discovery 2, L. Polkowski and A. Skowron (eds.) Physica-Verlag, (1998), pp. 471-487.
22. T. M. Khoshgoftaar, B. Cukic and N. Seliya, Predicting fault-prone modules in embedded systems using analogy-based classification models, International Journal of Software Engineering and Knowledge Engineering 12(2) World Scientific Publishing (2002) 201-221.
23. J. Bazan, M. Szczuka and J. Wroblewski, A new version of rough set exploration system, in Proceedings of the Third International Conference, RSCTC2002 (2002), pp. 397-404.
24. J. G. Bazan and M. S. Szczuka, RSES and REALIA a collection of tools for rough set computations, in Rough Sets and Current Trends in Computing, Springer Verlag, (2000), pp. 106-113.
25. S. H. Nguyen and H. S. Nguyen, Discretization methods in data mining, in Rough Sets in Knowledge Discovery 1, L. Polkowski and A. Skowron (eds.) Physica-Verlag, (1998), pp. 451-482.
26. S. H. Nguyen, A. Showron and P. Synak, Discovery of data patterns with applications to decomposition and classification problems, in Rough Sets in Knowledge Discovery 2, L. Polkowski and A. Skowron (eds.) Physica-Verlag (1998), pp. 55-97.
27. U. M. Fayyad, Data mining and knowledge discovery: Making sense out of data, IEEE Expert 11(4) (1996) 20-25.
28. J. P. Hudepohl, S. J. Aud, T. M. Khoshgoftaar, E. B. Allen and J. Mayrand, Emerald: Software metrics and models on the desktop, IEEE Software 13(5) (1996) 56-60.
29. R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, in Proceedings of International Joint Conference on Artificial Intelligence (1995), pp. 1137-1145.
30. T. M. Khoshgoftaar, X. Yuan and E. B. Allen, Balancing misclassification rates in classification tree models of software quality, Empirical Software Engineering 5(4) (2000) 313-330.