Feature selection in regression tasks using conditional mutual information

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6669 LNCS, Issue , 2011, Pages 224-231

  Latorre Carmona, Pedro ^a   Sotoca, José M ^a   Pla, Filiberto ^a   Phoa, Frederick K H ^b   Bioucas Dias, José ^c  

^a UNIVERSITAT JAUME I   (Spain)

^b ACADEMIA SINICA   (Taiwan)

^c INSTITUTO DE TELECOMUNICAÇÕES   (Portugal)

Author keywords

Conditional Density Estimation; Feature Selection; Information measures; Regression

Indexed keywords

AGGLOMERATIVE HIERARCHICAL CLUSTERING; CONDITIONAL DENSITY; CONDITIONAL MUTUAL INFORMATION; DATA SETS; DISSIMILARITY MATRIX; FEATURE SELECTION METHODS; INFORMATION MEASURES; REGRESSION; REGRESSION ESTIMATION; REGRESSION FUNCTION; REGRESSION PROBLEM; ROOT MEAN SQUARED ERRORS; SELECTION METHODS; SUPPORT VECTOR REGRESSIONS; SUPPORT VECTOR REGRESSION (SVR);

CLUSTERING ALGORITHMS; IMAGE ANALYSIS; INFORMATION USE; MULTI AGENT SYSTEMS; REGRESSION ANALYSIS; CLASSIFICATION (OF INFORMATION); MEAN SQUARE ERROR; PATTERN RECOGNITION;

FEATURE EXTRACTION;

EID: 79959999906     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-21257-4_28     Document Type: Conference Paper

References (21)

1. Cover, T.M., Thomas, J.A.: Elements of Information Theory. John Wiley & Sons Inc., Chichester (1991)
2. Drucker, H., Burges, C., Kaufman, L., Kaufman, L., Smola, A., Vapnik, V.: Support Vector Regression Machines. In: NIPS 1996, pp. 155-161 (1996)
3. Friedmann,M.: The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32(200), 675-701 (1937)
4. García, S., Fernández, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information Sciences 180, 2044-2064 (2010)
5. Gill, P.E., Murray, W., Wright, M.H.: Practical Optimization. Academic Press, London (1981)
6. Harrison, D., Rubinfeld, D.L.: Hedonic prices and the demand for clean air. J. Environ. Econ. Manag. 5, 81-102 (1978)
7. Holmes, M.P., Gray, A., Isbell, C.L.: Fast kernel conditional density estimation: A dual-tree Monte Carlo approach. Comput. Stat. Data Analysis 54(7), 1707-1718 (2010)
8. Hyndman, R.J., Bashtannyk, D.M., Grunwald, G.K.: Estimating and visualizing conditional densities. Journal of Computation and graphical Statistics 5(4), 315-336 (1996)
9. Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: A review. IEEE Trans. PAMI 22(1), 4-37 (2000)
10. Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: IEEE ICNN, pp. 1942-1948 (1995)
11. Kwak, N., Choi, C.-H.: Input feature selection by mutual information based on parzen window. IEEE Trans. PAMI 24(12), 1667-1671 (2002)
12. Dash, M., Liu, H.: Feature selection for classification. Intelligent Data Analysis 1, 131-156 (1997)
13. Monteiro, S.T., Kosugi, Y.: Particle Swarms for Feature Extraction of Hyperspectral Data. IEICE Trans. Inf. and Syst. E90D(7), 1038-1046 (2007)
14. Moreno, J.F.: Sen2flex data acquisition report, Universidad de Valencia, Tech. Rep. (2005)
15. Ney, H.: On the relationship between classification error bounds and training criteria in statistical pattern recognition. In: Perales, F.J., Campilho, A.C., Pérez, N., Sanfeliu, A. (eds.) IbPRIA 2003. LNCS, vol. 2652, pp. 636-645. Springer, Heidelberg (2003)
16. Pudil, P., Ferri, F.J., Novovicova, J., Kittler, J.: Floating search methods for feature selection with nonmonotonic criterion functions. Pattern Recognition 2, 279-283 (1994)
17. Rosenblatt, M.: Remarks on some nonparametric estimates of a density function. Ann. Math. Statist. 27, 832-837 (1956)
18. Sotoca, J.M., Pla, F.: Supervised feature selection by clustering using conditional mutual information-based distances. Pattern Recognition 43(6), 2068-2081 (2010)
19. Verleysen, M., Rossi, F., François, D.: Advances in Feature Selection with Mutual Information. In: Biehl, M., Hammer, B., Verleysen, M., Villmann, T. (eds.) Similarity-Based Clustering. LNCS, vol. 5400, pp. 52-69. Springer, Heidelberg (2009)
20. Ward, J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236-244 (1963)
21. Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. R. Statist. Soc. B 67(part 2), 301-320 (2005)