Software measurement data reduction using ensemble techniques

Neurocomputing

Volumn 92, Issue , 2012, Pages 124-132

  Wang, Huanjing ^a   Khoshgoftaar, Taghi M ^b   Napolitano, Amri ^b  

^a WESTERN KENTUCKY UNIVERSITY   (United States)

^b Florida Atlantic University   (United States)

Author keywords

Defect prediction; Ensembles of feature ranking techniques; Feature selection

Indexed keywords

CLASSIFICATION MODELS; DEFECT PREDICTION; DIFFERENT SIZES; EMPIRICAL STUDIES; ENSEMBLE TECHNIQUES; FAULT PREDICTION; FEATURE RANKING; FEATURE SELECTION METHODS; FEATURE SUBSET SELECTION; FILTER TECHNIQUES; LOCAL OPTIMA; MULTIPLE FEATURES; PROGRAM MODULE; SIGNAL TO NOISE; SOFTWARE DEFECT PREDICTION; SOFTWARE DEVELOPMENT PROCESS; SOFTWARE MEASUREMENT DATA; SOFTWARE METRICS;

CLASSIFICATION (OF INFORMATION); DATA REDUCTION; DEFECTS; MATHEMATICAL MODELS; MEASUREMENTS; SOFTWARE ENGINEERING;

FEATURE EXTRACTION;

ARTICLE; BAYESIAN LEARNING; CLASSIFICATION; CLASSIFIER; COMMUNICATION SOFTWARE; COMPUTER PROGRAM; FEATURE SELECTION; FILTER; K NEAREST NEIGHBOR; LOGISTIC REGRESSION ANALYSIS; PERCEPTRON; PREDICTION; PRIORITY JOURNAL; SIGNAL NOISE RATIO;

EID: 84861479227     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.08.040     Document Type: Article

References (33)

1. K. Gao, T.M. Khoshgoftaar, 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, pp. 272-277.
2. D. Rodriguez, R. Ruiz, J. Cuadrado-Gallego, J. Aguilar-Ruiz, Detecting fault modules applying feature selection to classifiers, in: Proceedings of 8th IEEE International Conference on Information Reuse and Integration, Las Vegas, Nevada, pp. 667-672.
3. Guyon I., Elisseeff A. An introduction to variable and feature selection. J. Mach. Learn. Res. 2003, 3:1157-1182.
4. Hall M.A., Holmes G. Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans. Knowl. Data Eng. 2003, 15:1437-1447.
5. Forman G. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 2003, 3:1289-1305.
6. Liu H., Yu L. Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowl. Data Eng. 2005, 17:491-502.
7. Chen Z., Menzies T., Port D., Boehm B. Finding the right data for software cost modeling. IEEE Software 2005, 38-46.
8. Witten I.H., Frank E. Data Mining: Practical Machine Learning Tools and Techniques 2005, Morgan Kaufmann. 2nd edition.
9. K. Lee, Combining multiple feature selection methods, in: Mid-Atlantic Student Workshop on Programming Languages and Systems (MASPLAS'02), pp. 12.1-12.9.
10. L. Rokach, B. Chizi, O. Maimon, Feature selection by combining multiple methods, in: Advances in Web Intelligence and Data Mining, 2006, pp. 295-304.
11. J.T. de Souza, N. Japkowicz, 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, pp. 667-674.
12. S. Loscalzo, L. Yu, 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, pp. 567-576.
13. J.O.S. Olsson, 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, pp. 798-799.
14. H. Wang, T.M. Khoshgoftaar, K. Gao, Ensemble feature selection technique for software quality classification, in: Proceedings of the 22nd International Conference on Software Engineering and Knowledge Engineering, Redwood City, CA, USA, pp. 215-220.
15. Plackett R.L. Karl Pearson and the chi-squared test. International Statistical Review 1983, 51:59-72.
16. K. Kira, L.A. Rendell, A practical approach to feature selection, in: Proceedings of 9th International Workshop on Machine Learning, pp. 249-256.
17. C.-H. Yang, C.-C. Huang, K.-C. Wu, H.-Y. Chang, A novel GA-Taguchi-based feature selection method, in: IDEAL '08: Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning, Berlin, Heidelberg, pp. 112-119.
18. Breiman L., Friedman J., Olshen R., Stone C. Classification and Regression Trees 1984, Chapman and Hall/CRC Press, Boca Raton, FL.
19. Peng H., Long F., Ding C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27:1226-1238.
20. Khoshgoftaar T.M., Seliya N. Fault-prediction modeling for software quality estimation: comparing commonly used techniques. Empirical Software Eng. J. 2003, 8:255-283.
21. Fawcett T. An introduction to ROC analysis. Pattern Recognition Lett. 2006, 27:861-874.
22. G.H. John, P. Langley, Estimating continuous distributions in Bayesian classifiers, in: Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, vol. 2, pp. 338-345.
23. Haykin S. Neural Networks: A Comprehensive Foundation 1998, Prentice-Hall. 2nd edition.
24. Aha D.W., Kibler D., Albert M.K. Instance-based learning algorithms. Mach. Learn. 1991, 6. 1573-0565.
25. Le Cessie S., Van Houwelingen J.C. Ridge estimators in logistic regression. Appl. Stat. 1992, 41:191-201.
26. Domingos P., Pazzani M. On the optimality of the simple Bayesian classifier under zero-one loss. Mach. Learn. 1997, 29:103-130.
27. T. Zimmermann, R. Premraj, A. Zeller, Predicting defects for eclipse, in: ICSEW '07: Proceedings of the 29th International Conference on Software Engineering Workshops, IEEE Computer Society, Washington, DC, USA, 2007, p. 76.
28. Koru A.G., Zhang D., Emam K.E., Liu H. An investigation into the functional form of the size-defect relationship for software modules. IEEE Trans. Software Eng. 2009, 35:293-304.
29. W. Altidor, T. Khoshgoftaar, J. Van Hulse, Classification with Feature Selection from Noisy Data: A Comparative Study of Filters, Technical Report, Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, May 2010.
30. Y. Jiang, J. Lin, B. Cukic, T. Menzies, Variance analysis in software fault prediction models, in: Proceedings of the 20th IEEE International Conference on Software Reliability Engineering, Bengaluru-Mysuru, India, pp. 99-108.
31. T.M. Khoshgoftaar, M. Golawala, 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, Washington, DC, USA, vol. 2, pp. 310-317.
32. Berenson M.L., Goldstein M., Levine D. Intermediate Statistical Methods and Applications: A Computer Package Approach 1983, Prentice-Hall, Englewood Cliffs, NJ. 2nd edition.
33. Gao K., Khoshgoftaar T.M., Wang H., Seliya N. Choosing software metrics for defect prediction: an investigation on feature selection techniques. Software: Pract. Exper. 2011, 41:579-606.