Feature selection with biased sample distributions

2009 IEEE International Conference on Information Reuse and Integration, IRI 2009

Volumn , Issue , 2009, Pages 23-28

  Kamal, Abu H M ^a   Zhu, Xingquan ^a,b   Pandya, Abhijit ^a   Hsu, Sam ^a  

^a Florida Atlantic University   (United States)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

Biased sample distributions; Classification; Feature selection; Imbalanced data

Indexed keywords

ACCURATE PREDICTION; BIASED SAMPLE DISTRIBUTIONS; BIOMEDICAL RESEARCH; CLASS LABELS; CLASSIFICATION; CREDIT CARD FRAUDS; DATA COLLECTION; DATA COLLECTION PROCESS; DATA SETS; EXPERIMENTAL COMPARISON; FEATURE SELECTION; FEATURE SELECTION METHODS; FILTERING TECHNIQUE; HIGHER WEIGHT; IMBALANCED DATA; NETWORK INTRUSIONS; RELIEFF; SAMPLE DISTRIBUTIONS;

INFORMATION USE; MATHEMATICAL MODELS; RESEARCH LABORATORIES;

DATA ACQUISITION;

EID: 70449387048     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IRI.2009.5211613     Document Type: Conference Paper

References (17)

1. Liu H. and Hiroshi M., Feature selection for knowledge discovery and data mining, Springer, 1998.
2. Guyou I., Elisseeff A., An introduction to variable and feature selection, J. of machine Learning Research 3:1157-1182, 2003.
3. Kwak N., Choi C., Input feature selection for classification problems, IEEE Trans. on Neural Networks, 13(1), 2002.
4. Golub T., et al., Molecular classification of cancer: class discovery and class prediction by gene expression monitoring, Science, 1, 1999.
5. Deonier, Richard C., Tavare Simon, Waterman, Michael S., Computational Genome Analysis, An Introduction, Springer 2007.
6. The Breast Center at Johns Hopkins, Researcher Pinpoint Genes Involved in Breast Cancer Growth, August, 2003 (http://www.hopkinsbreastcenter.org/artemis/ 200308/feature6.html),
7. C. D. Logsdon, D. M. Simeone, C. Binkley, T. Arumugam, J-K. Greenson, T. J. Giordano, D. E. Misek, and S. Hanash. Molecular profiling of pancreatic adenocarcinoma and chronic pancreatitis identifies multiple genes differentially regulated in pancreatic cancer. Cancer Research, 63:2649-2657, 2003.
8. Kohavi, R. and John, G. H., Wrappers for feature subset selection. Artificial Intelligence, 97:1-2, 1997.
9. Chen, X. and Wasikowski, M., FAST: a ROC-based feature selection metric for small samples and imbalanced data classification problems. In Proc. of the 14th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining, 2008.
10. Li T. et al., A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression, Bioinformatics, 20:2429-2437, 2004.
11. Robnik-Šikonja, M. and Kononenko, I. 2003. Theoretical and Empirical Analysis of ReliefF and RReliefF. Mach. Learn. 53, 2003.
12. nd Edition, 2003.
13. Kent Ridge, Kent Ridge Biomedical Data Set Repository, http://sdmc.i2r.a-star.edu.sg/rp/
14. Pedro D. & Pazzani M., On the optimality of the simple Bayesian classifier under zero-one loss. Mach. Learn., 297, 1997.
15. Breiman, Leo. Random Forests. Machine Learn. 45 (1), 5-32, 2001.
16. Aha, D. W., Kibler, D., and Albert, M. K. Instance-Based Learning Algorithms. Machine Learning 6, 1, 37-66, 1991.
17. Kamal A., Gene Selection for Sample Sets with Biased Distributions, Master Thesis, Florida Atlantic University, http://www.cse.fau.edu/~xqzhu/ students/akamal-thesis-2009.pdf.