On comparison of feature selection algorithms

AAAI Workshop - Technical Report

Volumn WS-07-05, Issue , 2007, Pages 34-39

  Refaeilzadeh, Payam ^a   Tang, Lei ^a   Liu, Huan ^a  

^a Arizona State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EVALUATION METHODS; FEATURE SELECTION; FEATURE SELECTION ALGORITHMS; FS ALGORITHMS; MACHINE-LEARNING; TECHNICAL REPORTS;

ARTIFICIAL INTELLIGENCE; ROBOT LEARNING;

LEARNING SYSTEMS;

EID: 52049116945     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Blum, A. L., and Langley, P. 1997. Selection of relevant features and examples in machine learning. AI97:245-271.
2. Bouckaert, R. R. 2003. Choosing between two learning algorithms based on calibrated tests. In ICML.
3. Bouckaert, R. R. 2004. Estimating replicability of classifier learning experiments. In ICML.
4. Dietterich, T. G. 1998. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 10(7): 1895-1923.
5. Dy, J. G., and Brodley, C. E. 2004. Feature selection for unsupervised learning. JMLR 5:845-889.
6. Guyon, I., and Elisseeff, A. 2003. An introduction to variable and feature selection. JMLR 3:1157-1182.
7. Jain, A., and Zongker, D. 1997. Feature selection: Evaluation, application, and small sample performance. IEEE TPAMI 19(2): 153-158.
8. John, G. H.; Kohavi, R.; and Pfleger, K. 1994. Irrelevant feature and the subset selection problem. In ICML.
9. Kohavi, R. 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection. In IJCAI.
10. Kononenko, I. 1994. Estimating attributes: Analysis and extensions of relief. In ECML, 171-182.
11. Liu, H., and Yu, L. 2005. Toward integrating feature selection algorithms for classification and clustering. IEEE TKDE 17:491-502.
12. L.Welch, B. 1947. The generalization of 'student's' problem when several different population variances are involved. Biometrika 34:28-35.
13. Molla, M.; Waddell, M.; Page, D.; and Shavlik, J. 2004. Using machine learning to design and interpret geneexpression microarrays. AI Mag. 25(1):23-44.
14. Nadeau, C., and Bengio, Y. 1999. Inference for the generalization error. In NIPS.
15. Reunanen, J. 2003. Overfitting in making comparisons between variable selection methods. JMLR 3:1371-1382.
16. Salzberg, S. 1997. On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery 1(3):317-328.
17. Thrun, S. B.; etal. 1991. The MONK's problems: A performance comparison of different learning algorithms. Technical Report CS-91-197, Pittsburgh, PA.
18. van 't Veer, L. J.; etal. 2002. gene expression profiling predicts clinical outcome of breast cancer. Nature.
19. Yu, L., and Liu, H. 2003. Feature selection for high-dimensional data: a fast correlation-based filter solution. In ICML