Robustness of filter-based feature ranking: A case study

Proceedings of the 24th International Florida Artificial Intelligence Research Society, FLAIRS - 24

Volumn , Issue , 2011, Pages 453-458

  Altidor, Wilker ^a   Khoshgoftaar, Taghi M ^a   Van Hulse, Jason ^a  

^a FLORIDA ATLANTIC UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION PERFORMANCE; FEATURE RANKING; FILTER MODEL; FILTER-BASED; GAIN RATIO; RELIEFF; SIGNAL TO NOISE;

ARTIFICIAL INTELLIGENCE; RESEARCH;

FEATURE EXTRACTION;

EID: 80052418039     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Asuncion, A., and Newman, D. 2007. UCI machine learning repository [http://www.ics.uci.edu/~mlearn/MLRepository.html]. University of California, Irvine, School of Information and Computer Sciences.
2. Fayyad, U. M., and Irani, K. B. 1992. On the handling of continuous-valued attributes in decision tree generation. Machine Learning 8:87-102.
3. Gilad-Bachrach, R.; Navot, A.; and Tishby, N. 2004. Margin based feature selection - theory and algorithms. In International Conference on Machine Learning (ICML), 43-50. ACM Press.
4. Guyon, I., and Elisseeff, A. 2003. An introduction to variable and feature selection. Journal of Machine Learning Research 3:1157-1182.
5. Hall, M. A., and Holmes, G. 2003. Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data Engineering 15(6):1437-1447.
6. Jin, C. L.; Ling, C. X.; Huang, J.; and Zhang, H. 2003. Auc: a statistically consistent and more discriminating measure than accuracy. In Proceedings of 18th International Conference on Artificial Intelligence, 329-341.
7. Kira, K., and Rendell, L. A. 1992. A practical approach to feature selection. In Proceedings of the ninth international workshop on Machine learning, 249-256. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
8. Kononenko, I. 1994. Estimating attributes: Analysis and extensions of RELIEF. In European Conference on Machine Learning, 171-182. Springer Verlag.
9. Lakshmi, K., and Mukherjee, D. S. 2006. An improved feature selection using maximized signal to noise ratio technique for TC. In Proceedings of the Third International Conference on Information Technology: New Generations, 541-546. Washington, DC, USA: IEEE Computer Society.
10. Liu, H.; Li, J.; and Wong, L. 2002. A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns. Genome Informatics 13:51-60.
11. Méndez, J. R.; Fdez-Riverola, F.; Díaz, F.; Iglesias, E. L.; and Corchado, J. M. 2006. A comparative performance study of feature selection methods for the anti-spam filtering domain. In Industrial Conference on Data Mining, 106-120.
12. Ruiz, R.; Aguilar-Ruiz, J. S.; Santos, J. C. R.; and Díaz- Díaz, N. 2005. Analysis of feature rankings for classification. In IDA, 362-372.
13. Saeys, Y.; Inza, I. n.; and Larrañaga, P. 2007. A review of feature selection techniques in bioinformatics. Bioinformatics 23(19):2507-2517.
14. Van Hulse, J., and Khoshgoftaar, T. 2009. Knowledge discovery from imbalanced and noisy data. Data and Knowledge Engineering 68(12):1513-1542.
15. Wang, X., and Gotoh, O. 2009. Accurate molecular classification of cancer using simple rules. BMC Medical Genomics 2(1):64+.
16. Wang, H.; Khoshgoftaar, T.; and Gao, K. 2010. Ensemble feature selection technique for software quality classification. In Proceedings of the 22nd International Conference on Software Engineering and Knowledge Engineering, 215-220.
17. Witten, I. H., and Frank, E. 2005. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2 edition.
18. Xiong, H.; Pandey, G.; Steinbach, M.; and Kumar, V. 2006. Enhancing data analysis with noise removal. IEEE Transactions on Knowledge and Data Engineering 18(3):304-319.
19. Yang, Y., and Pedersen, J. O. 1997. A comparative study on feature selection in text categorization. In Fisher, D. H., ed., Proceedings of ICML-97, 14th International Conference on Machine Learning, 412-420. Nashville, US: Morgan Kaufmann Publishers, San Francisco, US.