Outlier resistant PCA ensembles

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

Volumn 4253 LNAI - III, Issue , 2006, Pages 432-440

  Gabrys, Bogdan ^a   Baruque, Bruno ^b   Corchado, Emilio ^b  

^a BOURNEMOUTH UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF BURGOS   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); LEARNING ALGORITHMS; LEARNING SYSTEMS; STATISTICAL METHODS;

OUTLIER RESISTANT; PCA ALGORITHMS; PREDICTION SYSTEMS; PREDICTOR ENSEMBLES;

PRINCIPAL COMPONENT ANALYSIS;

EID: 33750729985     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11893011_55     Document Type: Conference Paper

References (13)

1. Pearson, K. On lines and planes of closest fit to systems of points in space. Philosophical Magazine 2:559-572. (1901).
2. Hotelling, H. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24:417-441,498-520. (1933).
3. Cook, R. D. Detection of influential observations in linear regression. Technometrics 19, 15-18. (1977).
4. Dixon, W. J. Analysis of extreme values, Ann. Math. Stat., 21, 488-506. (1950)
5. Oja, E. Neural networks, principal components and subspaces. International Journal of Neural Systems 1(1):61-68. (1989).
6. Sanger, D. Contribution analysis: A technique for assigning responsibilities to hidden units in connectionist networks. Connection Science, 1:115-138. (1989).
7. Breiman, L. Bagging predictors. Machine Learning, 24:123-140. (1996).
8. Schapire, R.E; Freud, Y; Bartlett, P. and Lee, W.S. Boosting the margin: a new explanation for the effectiveness of voting methods. The Annals of Statistics, 26(5): 1651-1686, 1998.
9. Gabrys, B. Combining neuro-fuzzy classifiers for improved generalisation and reliability. In Proceedings the Int. Joint Conference on Neural Networks (IJCNN'2002) a part of the WCCI'2002 Congress, pages 2410-2415, Honolulu, USA, 2002.
10. Kuncheva, L, Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience, 2004.
11. Ruta, D. and B.Gabrys, Classifier Selection for Majority Voting, Special issue of the journal of information fusion on Diversity in Multiple Classifier Systems, vol. 6, issue 1, pp. 63-81, 1 March 2005.
12. Gabrys, B., Learning Hybrid Neuro-Fuzzy Classifier Models From Data: To Combine or not to Combine?, Fuzzy Sets and Systems, vol. 147, pp. 39-56, 2004.
13. Ruta, D. and B. Gabrys, A Theoretical Analysis of the Limits of Majority Voting Errors for Multiple Classifier Systems, Pattern Analysis and Applications, vol. 5, pp. 333-350, 2002.