Skurichina, M.[Marina],
Duin, R.P.W.[Robert P.W.],
Bagging for Linear Classifiers,
PR(31), No. 7, July 1998, pp. 909-930.
Elsevier DOI
9807
BibRef
Earlier:
Stabilizing Classifiers for Very Small Sample Sizes,
ICPR96(II: 891-896).
IEEE DOI
9608
(TU Delft, NL)
BibRef
Hothorn, T.[Torsten],
Lausen, B.[Berthold],
Double-bagging: combining classifiers by bootstrap aggregation,
PR(36), No. 6, June 2003, pp. 1303-1309.
Elsevier DOI
0304
BibRef
Bryll, R.[Robert],
Gutierrez-Osuna, R.[Ricardo],
Quek, F.K.H.[Francis K.H.],
Attribute bagging: improving accuracy of classifier ensembles by using
random feature subsets,
PR(36), No. 6, June 2003, pp. 1291-1302.
Elsevier DOI
0304
BibRef
Nanni, L.[Loris],
Lumini, A.[Alessandra],
FuzzyBagging: A novel ensemble of classifiers,
PR(39), No. 3, March 2006, pp. 488-490.
Elsevier DOI
0601
BibRef
Nanni, L.[Loris],
Lumini, A.[Alessandra],
Ensemblator: An ensemble of classifiers for reliable classification of
biological data,
PRL(28), No. 5, 1 April 2007, pp. 622-630.
Elsevier DOI
0703
Ensemble of classifiers; Machine learning; Bioinformatics
BibRef
Shin, H.W.,
Sohn, S.Y.,
Selected tree classifier combination based on both accuracy and error
diversity,
PR(38), No. 2, February 2005, pp. 191-197.
Elsevier DOI
0411
Build tree classifier, cluster them.
BibRef
Sohn, S.Y.,
Shin, H.W.,
Experimental study for the comparison of classifier combination methods,
PR(40), No. 1, January 2007, pp. 33-40.
Elsevier DOI
0611
Bagging; Random subspace method; Classifier selection; Parametric fusion
BibRef
Martínez-Muñoz, G.[Gonzalo],
Suárez, A.[Alberto],
Using boosting to prune bagging ensembles,
PRL(28), No. 1, 1 January 2007, pp. 156-165.
Elsevier DOI
0611
Machine learning; Decision trees; Bagging; Boosting;
Ensembles; Ensemble pruning
BibRef
Martínez-Muñoz, G.[Gonzalo],
Suárez, A.[Alberto],
Switching class labels to generate classification ensembles,
PR(38), No. 10, October 2005, pp. 1483-1494.
Elsevier DOI
0508
BibRef
Martinez-Munoz, G.[Gonzalo],
Suarez, A.[Alberto],
Out-of-bag estimation of the optimal sample size in bagging,
PR(43), No. 1, January 2010, pp. 143-152.
Elsevier DOI
0909
Bagging; Subagging; Bootstrap sampling; Subsampling; Optimal sampling
ratio; Ensembles of classifiers; Decision trees
BibRef
Sun, D.[Dan],
Zhang, D.Q.[Dao-Qiang],
Bagging Constraint Score for feature selection with pairwise
constraints,
PR(43), No. 6, June 2010, pp. 2106-2118.
Elsevier DOI
1003
Feature selection; Constraint Score; Pairwise constraints; Bagging;
Ensemble learning
BibRef
Zhu, X.Q.[Xing-Quan],
Yang, Y.[Ying],
A lazy bagging approach to classification,
PR(41), No. 10, October 2008, pp. 2980-2992.
Elsevier DOI
0808
Classification; Classifier ensemble; Bagging; Lazy learning
BibRef
Tzeng, Y.C.[Yu-Chang],
Fan, K.T.[Kou-Tai],
Chen, K.S.[Kun-Shan],
An Adaptive Thresholding Multiple Classifiers System for Remote Sensing
Image Classification,
PhEngRS(75), No. 6, June 2009, pp. 679-688.
WWW Link.
0910
Bagging and/or Boosting Weighted Multiple Classifiers Systems with an
Adaptive Thresholding for Remote Sensing Image Classification
BibRef
Zaman, F.[Faisal],
Hirose, H.[Hideo],
Effect of Subsampling Rate on Subbagging and Related Ensembles of
Stable Classifiers,
PReMI09(44-49).
Springer DOI
0912
BibRef
Kudo, M.[Mineichi],
Nakamura, A.[Atsuyoshi],
Takigawa, I.[Ichigaku],
Classification by reflective convex hulls,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Shidara, Y.[Yohji],
Kudo, M.[Mineichi],
Nakamura, A.[Atsuyoshi],
Classification by bagged consistent itemset rules,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Shirai, S.[Satoshi],
Kudo, M.[Mineichi],
Nakamura, A.[Atsuyoshi],
Bagging, Random Subspace Method and Biding,
SSPR08(801-810).
Springer DOI
0812
BibRef
Su, X.Y.[Xiao-Yuan],
Khoshgoftarr, T.M.[Taghi M.],
Zhu, X.Q.[Xing-Quan],
VoB predictors: Voting on bagging classifications,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Zhu, X.Q.[Xing-Quan],
Bao, C.Y.[Cheng-Yi],
Qiu, W.D.[Wei-Dong],
Bagging very weak learners with lazy local learning,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Ñanculef, R.[Ricardo],
Valle, C.[Carlos],
Allende, H.[Héctor],
Moraga, C.[Claudio],
Bagging with Asymmetric Costs for Misclassified and Correctly
Classified Examples,
CIARP07(694-703).
Springer DOI
0711
BibRef
Chawla, N.[Nitesh],
Moore, Jr., T.E.[Thomas E.],
Bowyer, K.W.[Kevin W.],
Hall, L.O.[Lawrence O.],
Springer, C.[Clayton], and
Kegelmeyer, P.[Philip],
Bagging Is a Small-Data-Set Phenomenon,
CVPR01(II:684-689).
IEEE DOI
0110
Form a committee of classifiers from subsets rather then
use bagging.
BibRef
Draper, B.A.[Bruce A.],
Baek, K.[Kyungim],
Bagging in Computer Vision,
CVPR98(144-149).
IEEE DOI Multiple predictors
BibRef
9800
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Mixture of Experts, Multiple Classifiers, Combining Classifiers .