Ensemble feature selection technique for software quality classification

SEKE 2010 - Proceedings of the 22nd International Conference on Software Engineering and Knowledge Engineering

Volumn , Issue , 2010, Pages 215-220

  Wang, Huanjing ^a   Khoshgoftaar, Taghi M ^b   Gao, Kehan ^c  

^a WESTERN KENTUCKY UNIVERSITY   (United States)

^b FLORIDA ATLANTIC UNIVERSITY   (United States)

^c EASTERN CONNECTICUT STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ACCURACY; CLASSIFICATION MODELS; DATA MINING APPLICATIONS; DATA PREPROCESSING; EMPIRICAL STUDIES; ENSEMBLE FEATURE SELECTIONS; ENSEMBLE TECHNIQUES; FEATURE RANKING; FEATURE SELECTION; FILTER-BASED; PERFORMANCE METRICES; RANK ORDERING; RECEIVER OPERATING CHARACTERISTIC CURVES; REDUCED DATA; SOFTWARE PROJECT; SOFTWARE QUALITY; SOFTWARE QUALITY ENGINEERING;

CLASSIFICATION (OF INFORMATION); COMPUTER SOFTWARE SELECTION AND EVALUATION; FILTRATION; KNOWLEDGE ENGINEERING; MODELS; SOFTWARE ENGINEERING;

FEATURE EXTRACTION;

EID: 79952374025     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. W. Altidor, T. M. Khoshgoftaar, and J. V. Hulse, "An empirical study on wrapper-based feature ranking, " in Proceedings of 21st IEEE International Conference on Tools with Artificial Intelligence, Newark, NJ, USA, Nov. 2-5 2009, pp. 75-82.
2. K. Gao, T. M. Khoshgoftaar, and A. Napolitano, "Exploring software quality classification with a wrapper-based feature ranking technique, " in Proceedings of 21st IEEE International Conference on Tools with Artificial Intelligence, Newark, NJ, USA, Nov. 2-5 2009, pp. 67-74.
3. H. Wang, T. M. Khoshgoftaar, K. Gao, and N. Seliya, "Mining data from multiple software development projects, " in Proceedings of 9th IEEE International Conference on Data Mining - Workshops, 2009, pp. 551-557.
4. J. O. S. Olsson and D. W. Oard, "Combining feature selectors for text classification, " in CIKM'06: Proceedings of the 15th ACM international conference on Information and knowledge management, New York, NY, USA, 2006, pp. 798-799. (Pubitemid 47203634)
5. Y. Saeys, T. Abeel, and Y. Peer, "Robust feature selection using ensemble feature selection techniques, " in ECML PKDD'08: Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II. Berlin, Heidelberg: Springer-Verlag, 2008, pp. 313-325.
6. K. Lee, "Combining multiple feature selection methods, " in Mid- Atlantic Student Workshop on Programming Languages and Systems( MASPLAS'02), 2002, pp. 12.1-12.9.
7. L. Rokach, B. Chizi, and O. Maimon, "Feature selection by combining multiple methods, " in Advances in Web Intelligence and Data Mining, 2006, pp. 295-304. (Pubitemid 44426140)
8. J. T. de Souza, N. Japkowicz, and S. Matwin, "Stochfs: A framework for combining feature selection outcomes through a stochastic process, " in Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, 2005, pp. 667-674.
9. S. Loscalzo, L. Yu, and C. Ding, "Consensus group stable feature selection, " in KDD'09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA, 2009, pp. 567-576.
10. A. C. Cameron and P. K. Trivedi, Regression Analysis of Count Data. Cambridge University Press, 1998.
11. J. R. Quinlan, "Induction of decision trees, " Machine Learning, vol. 1, no. 1, pp. 81-106, 1986.
12. M. A. Hall and G. Holmes, "Benchmarking attribute selection techniques for discrete class data mining, " IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 6, pp. 1437 - 1447, Nov/Dec 2003.
13. K. Kira and L. A. Rendell, "A practical approach to feature selection, " in Proceedings of 9th International Workshop on Machine Learning, 1992, pp. 249-256.
14. I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed. Morgan Kaufmann, 2005.
15. G. H. John and P. Langley, "Estimating continuous distributions in bayesian classifiers, " in Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, vol. 2, San Mateo, 1995, pp. 338-345.
16. D. W. Aha, D. Kibler, and M. K. Albert, "Instance-based learning algorithms, " Machine Learning, vol. 6, no. 1, pp. 1573-0565, January 1991.
17. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, 2000.
18. Y. Jiang, J. Lin, B. Cukic, and T. Menzies, "Variance analysis in software fault prediction models, " Software Reliability Engineering, International Symposium on, vol. 0, pp. 99-108, 2009.
19. T. Fawcett, "An introduction to ROC analysis, " Pattern Recognition Letters, vol. 27, no. 8, pp. 861-874, June 2006.
20. K. Gao, T. M. Khoshgoftaar, and H. Wang, "An empirical investigation of filter attribute selection techniques for software quality classification, " in Proceedings of the 10th IEEE International Conference on Information Reuse and Integration, Las Vegas, Nevada, August 10-12 2009, pp. 272-277.
21. A. G. Koru, D. Zhang, K. E. Emam, and H. Liu, "An investigation into the functional form of the size-defect relationship for software modules, " IEEE Trans. Software Eng., vol. 35, no. 2, pp. 293-304, 2009.
22. T. M. Khoshgoftaar, M. Golawala, and J. Van Hulse, "An empirical study of learning from imbalanced data using random forest, " in Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence, vol. 2, Washington, DC, USA, 2007, pp. 310-317.
23. H. Wang, T. M. Khoshgoftaar, K. Gao, and N. Seliya, "High- dimensional software engineering data and feature selection, " in Proceedings of 21st IEEE International Conference on Tools with Artificial Intelligence, Newark, NJ, USA, Nov. 2-5 2009, pp. 83-90.