Disturbing neighbors diversity for decision forests

Studies in Computational Intelligence

Volumn 245, Issue , 2009, Pages 113-133

  Maudes, Jesús ^a   Rodríguez, Juan J ^a   García osorio, César ^a  

^a UNIVERSITY OF BURGOS   (Spain)

Author keywords

Diversity; Ensemble of decision trees; Kappa Error movement diagrams

Indexed keywords

EID: 70350229993     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-03999-7_7     Document Type: Conference Paper

References (17)

1. Aha, D., Kibler, D.,Albert, M.K.: Instance-based learning algorithms.Mach. Learn. 6(1), 37-66 (1991)
2. Asuncion, A., Newman, D.J.: UCI machine learning repository., http://www.ics.uci.edu/~mlearn/MLRepository.html
3. Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Mach. Learn. 36(1-2), 105-139 (1999)
4. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123-140 (1996)
5. Breiman, L.: Random forests. Mach. Learn. 45(1), 5-32 (2001)
6. Bruno Caprile, B., Merler, S., Furlanello, C., Jurman, G.: Exact bagging with k-nearest neighbour classifiers. In: Roli, F., Kittler, J., Windeatt, T. (eds.) MCS 2004. LNCS, vol. 3077, pp. 72-81. Springer, Heidelberg (2004)
7. Dietterich, T.G.: Approximate statistical test for comparing supervised classification learning algorithms. Neural Comp. 10(7), 1895-1923 (1998)
8. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1-30 (2006)
9. Domeniconi, C., Yan, B.: Nearest neighbor ensemble. In: Kittler, J., Petrou, M., Nixon, M.S. (eds.) Proc. 17th Int. Conf. Patt. Recogn., Cambridge, UK, pp. 228-231. IEEE Comp. Soc., Los Alamitos (2004)
10. Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Saitta, L. (ed.) Proc. 13th Int. Conf. Mach. Learn., Bari, Italy, pp. 148-156. Morgan Kaufmann, San Francisco (1996)
11. Gama, J., Brazdil, P.: Cascade generalization. Mach. Learn. 41(3), 315-343 (2000)
12. Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Patt. Analysis Mach. Intell. 20(8), 832-844 (1998)
13. Kuncheva, L.,Whitaker, C.J.: Using diversity with three variants of boosting: Aggressive, conservative, and inverse. In: Roli, F., Kittler, J. (eds.) MCS 2002. LNCS, vol. 2364, pp. 81-90. Springer, Heidelberg (2002)
14. Margineantu, D.D., Dietterich, T.G.: Pruning adaptive boosting. In: Fisher, D.H. (ed.) Proc. 14th Int. Conf. Mach. Learn., Nashville, TN, pp. 211-218. Morgan Kaufmann, San Francisco (1997)
15. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kauffman, San Francisco (1993)
16. Webb, G.IA: MultiBoosting: a technique for combining boosting and wagging. Mach. Learn. 40(2), 159-196 (2000)
17. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2005)