Combining bagging, boosting, rotation forest and random subspace methods

Artificial Intelligence Review

Volumn 35, Issue 3, 2011, Pages 223-240

  Kotsiantis, Sotiris ^a  

^a UNIVERSITY OF PATRAS   (Greece)

Author keywords

Data mining; Ensembles of classifiers; Machine learning; Pattern recognition

Indexed keywords

BENCHMARK DATASETS; COMBINING METHOD; ENSEMBLE METHODS; ENSEMBLES OF CLASSIFIERS; MACHINE LEARNING; NOISE FREE DATA; RANDOM SUBSPACE METHOD; RESAMPLING;

ADAPTIVE BOOSTING; ALGORITHMS; PATTERN RECOGNITION; ROTATION;

LEARNING SYSTEMS;

EID: 79955962699     PISSN: 02692821     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10462-010-9192-8     Document Type: Article

References (39)

1. Bauer E, Kohavi R (1999) An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Mach Learn 36:105-139
2. Bosch A, Daelemans W (1999) Memory-based morphological analysis. In: Proceedings of 37th annual meeting of the ACL. University of Maryland, pp 285-292 (http://ilk.kub.nl/~antalb/ltuia/week10.html)
3. Breiman L (1996) Bagging predictors. Mach Learn 24(3):123-140 (Pubitemid 126724382)
4. Breiman L (2001) Random forests. Mach Learn 45(1):5-32
5. Bryll R, Gutierrez-Osuna R, Quek F (2003) Attribute bagging: improving accuracy of classifier ensemble by using random feature subsets. Pattern Recognit 36:1291-1302 (Pubitemid 36225868)
6. Bühlman P, Yu B (2002) Analyzing bagging. Ann Stat 30:927-961 (Pubitemid 37095335)
7. Buja WS (2006) Observations on bagging. Statistica Sinica 16:323-351
8. Cai Q-T, Peng C-Y, Zhang C-S (2008) A weighted subspace approach for improving bagging performance. In: IEEE ICASSP, pp 3341-3344
9. Dietterich T (2000) An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach Learn 40:139-157
10. Dietterich TG (2001) Ensemble methods in machine learning. In: Kittler J, Roli F (eds) Multiple classifier systems. LNCS, vol 1857, pp 1-15
11. Domingos P, Pazzani M (1997) On the optimality of the simple Bayesian classiier under zero-one loss. Mach Learn 29:103-130 (Pubitemid 127510035)
12. Frank E, Witten IH (1998) Generating Accurate Rule Sets Without Global Optimization. In: Fifteenth international conference on machine learning, pp 144-151
13. Frank A, Asuncion A (2010) UCI machine learning repository [http://www.archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science
14. Freidman JH, Hall P (2007) On bagging and nonlinear estimation. J Stat Plan Inference 137(3):669-683 (Pubitemid 44660748)
15. Freidman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view ofboosting. Ann Stat 28:307-337
16. Freund Y, Robert E Schapire (1996) Experiments with a new boosting algorithm. In: Proceedings of ICML'96, pp 148-156
17. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119-139 (Pubitemid 127433398)
18. Fumera G, Roli F, Serrau A (2005) Dynamics of variance reduction in bagging and other techniques based on randomisation. MCS 2005, LNCS vol 3541, pp 316-325 (Pubitemid 41422630)
19. Fumera G, Roli F, Serrau A (2008) A theoretical analysis of bagging as a linear combination of classiiers. IEEE Trans Pattern Anal Mach Intell 30(7):1293-1299
20. Garcia-Pedrajas N, Ortiz-Boyer D (2008) Boosting random subspace method. Neural Netw 21:1344-1362
21. Grandvalet Y (2004) Bagging equalizes influence. Mach Learn 55:251-270
22. Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832-844 (Pubitemid 128741345)
23. Kotsiantis S, Pierrakeas C, Pintelas P (2003) Preventing student dropout in distance learning systems using machine learning techniques. In: Proceedings of 7th international conference on knowledge-based intelligent information and engineering systems (KES), Oxford, Sept. 3-5, Lecture notes series, vol 2774. Springer, pp 267-274 (Pubitemid 37142734)
24. Kuncheva LI, Skurichina M, Duin RPW (2002) An experimental study on diversity for bagging and boosting with linear classiiers. Inf Fusion 3:245-258
25. Latinne P, Debeir O, Decaestecker Ch (2000) Mixing bagging and multiple feature subsets to improve classification accuracy of decision tree combination. In: Proceedings of the 10th Belgian-Dutch conference on machine learning, Tilburg University
26. Melville P, Mooney R (2003) Constructing diverse classifier ensembles using artificial training examples. In: Proceedings of IJCAI-2003, pp 505-510, Acapulco, Mexico, August 2003
27. Murthy (1998) Automatic construction of decision trees from data: a multi-disciplinary survey. Data Min Knowl Discov 2:345-389 (Pubitemid 128695483)
28. Opitz D, Maclin R (1999) Popular ensemble methods: an empirical study. Artif Intell Res 11:169-198 (Pubitemid 129628763)
29. Panov P, Dzeroski S (2007) Combining bagging and random subspaces to create better ensembles, IDA 2007, LNCS 4723, pp 118-129
30. Redpath DB, Lebart K (2005) Boosting feature selection. In: The third international conference on advances in pattern recognition, Bath, UK, Springer, Berlin, pp 305-314 (Pubitemid 41520570)
31. Rodríguez JJ, Kuncheva LI, Alonso CJ (2006) Rotation forest: a new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 28(10):1619-1630 (Pubitemid 46405081)
32. Salzberg S (1997) On comparing classiiers: pitfalls to avoid and a recommended approach. Data Min Knowl Discov 1:317-328
33. Schapire RE, Freund Y, Bartlett P, Lee WS (1998) Boosting the margin: a new explanation for the effectiveness of voting methods. Ann Stat 26:1651-1686 (Pubitemid 128376902)
34. Schapire RE, Singer Y (1999) Improved boosting algorithms using conidence-rated predictions. Mach Learn 37:297-336 (Pubitemid 32210620)
35. Shirai S, Kudo M, Nakamura A (2008) Bagging, random subspace method and biding. SSPR&SPR 2008, LNCS vol 5342, pp 801-810
36. Stefanowski J (2007) Combining answers of sub-classiiers in the bagging-feature ensembles, RSEISP 2007, LNAI vol 4585, pp 574-583
37. Ting KM, Witten IH (1997) Stacking bagged and dagged models. In: Fourteenth international conference on machine learning, San Francisco, CA, pp 367-375
38. Webb GI (2000) MultiBoosting: a technique for combining boosting and wagging. Mach Learn 40:159-196
39. Yin X-C, Liu C-P, Zhi H (2005) Feature combination using boosting. Pattern Recognit Lett 26:2195-2205 (Pubitemid 41383619)