Gonzalo Martínez-Muñoz, Alberto Suárez  Home/Search
Context
Related
| eps.uam.es/~gonzal...ia2004_preprint.ps Cached: PS.gz PS PDF This document uses CoBlitz to cache paper downloads. If your firewall is blocking outgoing connections to port 3125, you can use these links to download local copies.
Image Update HelpPS.gz PS PDF From: eps.uam.es/~gonzalo/publi...index (more) (Enter author homepages) |
Rate this article:      (best)Comment on this article |
Abstract: The order in which classifiers are aggregated in ensemble methods can be an important tool in the identification of subsets of classifiers that, when combined, perform better than the whole ensemble. Ensembles with randomly ordered classifiers usually exhibit a generalization error that decreases as the number of classifiers that are aggregated increases. If an appropriate order for aggregation is chosen, the generalization error reaches, at intermediate numbers of classifiers, a minimum, which ... (Update)
Active bibliography (related documents): More All
0.6: Using Boosting to Prune Bagging Ensembles - Suarez (Correct)
0.5: Multi-Class Protein Fold Classification Using a New.. - Tan, Gilbert, Deville (2003) (Correct)
0.4: Switching Class Labels to Generate Classification Ensembles - Martínez-Muñoz, Suárez (2005) (Correct)
Similar documents based on text: More All
0.3: Pruning in Ordered Bagging Ensembles - Suarez (2005) (Correct)
0.2: Prof. Dr. Bai Ling Wang - Bwang Maths Warwick (2001) (Correct)
0.2: Wavelet Based Decomposition Of Natural Images In Independent.. - Turiel, Parga (Correct)
BibTeX entry: (Update)
@inproceedings{ martinez04aggregation,
author = "Gonzalo Mart{\'{\i}}nez-Mu{\~n}oz and Alberto Su{\'a}rez",
title = "Aggregation Ordering in Bagging",
booktitle ="Proc. of the {IASTED} International Conference on Artificial Intelligence and Applications",
publisher = "Acta Press",
pages = "258- 263",
year = "2004"
};
Citations (may not include all citations):1262 Classification and Regression Trees (context) - Breiman, Friedman et al. - 1984
696 UCI repository of machine learning databases (context) - Blake, Merz
657 Bagging predictors - Breiman - 1996 ACM DBLP
509 A decision-theoretic generalization of on-line learning and .. - Freund, Schapire - 1995 ACM DBLP
243 Boosting the margin: A new explanation for the effectiveness.. - Schapire, Freund et al. - 1998 DBLP
155 An empirical comparison of voting classification algorithms:.. - Bauer, Kohavi - 1999
134 An experimental comparison of three methods for constructing.. - Dietterich - 2000
98 Machine Learning (context) - Breiman, forests - 2001 ACM DBLP
89 and arcing classifiers (context) - Breiman, variance - 1996
80 Arcing classifiers - Breiman - 1998
62 Pruning adaptive boosting - Margineantu, Dietterich - 1997 ACM DBLP
36 Soft margins for AdaBoost - Ratsch, Onoda et al. - 2001 ACM DBLP
28 Combining Artificial Neural Nets: Ensemble and Modular Multi.. (context) - Sharkey - 1999
14 Ensembling neural networks: Many could be better than all - Zhou, Wu et al. - 2002 DBLP
9 the boosting pruning problem - Tamon, Xiang - 2000
9 Cost complexitybased pruning of ensemble classifiers - Prodromidis, Stolfo - 2001
3 Using all data to generate decision tree ensembles (context) - Martnez-Munoz, Suarez - 2003
2 th National Conference on Artificial Intelligence (context) - Quinlan, boosting et al. - 1996
1 Lecture Notes in Artificial Intelligence (context) - Zhou, Tang et al. - 2003
Documents on the same site (http://www.eps.uam.es/~gonzalo/publications/index.html): More
Using Boosting to Prune Bagging Ensembles - Suarez (Correct)
Pruning in Ordered Bagging Ensembles - Suarez (2005) (Correct)
Switching Class Labels to Generate Classification Ensembles - Martínez-Muñoz, Suárez (2005) (Correct)
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC