Random projections for linear SVM ensembles

Applied Intelligence

Volumn 34, Issue 3, 2011, Pages 347-359

  Maudes, Jesús ^a   Rodríguez, Juan J ^a   García Osorio, César ^a   Pardo, Carlos ^a  

^a UNIVERSITY OF BURGOS   (Spain)

Author keywords

Diversity; Ensembles; Kappa error relative movement diagrams; Random projections; Rotation forests; SVMs

Indexed keywords

DIVERSITY; ENSEMBLES; RANDOM PROJECTIONS; RELATIVE MOVEMENT; SVMS;

ROTATION; SUPPORT VECTOR MACHINES;

PRINCIPAL COMPONENT ANALYSIS;

EID: 79956157044     PISSN: 0924669X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10489-011-0283-2     Document Type: Conference Paper

References (22)

1. D Achlioptas 2003 Database-friendly random projections: Johnson-Lindenstrauss with binary coins J Comput Syst Sci 66 4 671 687 2005771 1054.68040 10.1016/S0022-0000(03)00025-4
2. Bingham E, Mannila H (2001) Random projection in dimensionality reduction: applications to image and text data. In: Knowledge Discovery and Data Mining. ACM, New York, pp 245-250
3. L Breiman 1996 Bagging predictors Mach Learn 24 2 123 140 1425957 0858.68080 (Pubitemid 126724382)
4. J Cohen 1960 A coefficient of agreement for nominal scales Educ Psychol Meas 20 1 37 46 10.1177/001316446002000104
5. Dasgupta S, Freund Y (2008) Random projection trees and low dimensional manifolds. In: STOC '08: proceedings of the 40th annual ACM symposium on theory of computing. ACM, New York, pp 537-546
6. J Demšar 2006 Statistical comparisons of classifiers over multiple data sets J Mach Learn Res 7 1 30 2274360 (Pubitemid 43022939)
7. TG Dietterich 1998 Approximate statistical test for comparing supervised classification learning algorithms Neural Comput 10 7 1895 1923 10.1162/089976698300017197 (Pubitemid 128463689)
8. RE Fan KW Chang CJ Hsieh XR Wang CJ Lin 2008 Liblinear: a library for large linear classification J Mach Learn Res 9 1871 1874
9. Fradkin D, Madigan D (2003) Experiments with random projections for machine learning. In: KDD '03: proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 517-522
10. Frank A, Asuncion A (2010) UCI machine learning repository. URL http://archive.ics.uci.edu/ml
11. Y Freund RE Schapire 1997 A decision-theoretic generalization of on-line learning and an application to boosting J Comput Syst Sci 55 1 119 139 1473055 0880.68103 10.1006/jcss.1997.1504 (Pubitemid 127433398)
12. M Hall E Frank G Holmes B Pfahringer P Reutemann IH Witten 2009 The weka data mining software: an update SIGKDD Explor 11 1 10 18 10.1145/1656274.1656278
13. TK Ho 1998 The random subspace method for constructing decision forests IEEE Trans Pattern Anal Mach Intell 20 8 832 844 10.1109/34.709601 (Pubitemid 128741345)
14. Johnson W, Lindenstrauss J (1984) Extensions of Lipschitz maps into a Hilbert space. In: Conference in modern analysis and probability (1982, Yale University). Contemporary mathematics, vol 26. AMS, New York, pp 189-206
15. Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley-Interscience, New York
16. Kuncheva LI, Rodríguez JJ (2007) An experimental study on rotation forest ensembles. In: 7th international workshop on multiple classifier systems, MCS 2007. LNCS, vol 4472. Springer, Berlin, pp 459-468 (Pubitemid 350270641)
17. Margineantu DD, Dietterich TG (1997) Pruning adaptive boosting. In: Proc. 14th international conference on machine learning. Morgan Kaufmann, San Mateo, pp 211-218
18. Maudes J, Rodríguez JJ, García-Osorio C (2009) Disturbing neighbors diversity for decision forests. In: Okun O, Valentini G (eds) Applications of supervised and unsupervised ensemble methods. Studies in computational intelligence, vol 245. Springer, Berlin, pp 113-133
19. JJ Rodríguez LI Kuncheva CJ Alonso 2006 Rotation forest: A new classifier ensemble method IEEE Trans Pattern Anal Mach Intell 28 10 1619 1630 10.1109/TPAMI.2006.211 (Pubitemid 46405081)
20. Schclar A, Rokach L (2009) Random projection ensemble classifiers. In: Enterprise information systems 11th international conference proceedings. Lecture notes in business information processing, pp 309-316
21. Vapnik VN (1999) The nature of statistical learning theory. Information science and statistics. Springer, Berlin
22. GI Webb (2000) Multiboosting: a technique for combining boosting and wagging. Mach Learn 40(2)