14.1.5.2.1 Bagging, Combinations, Classifiers

Chapter Contents (Back)
Bagging. Classifer Combinations.

Skurichina, M.[Marina], Duin, R.P.W.[Robert P.W.],
Bagging for Linear Classifiers,
PR(31), No. 7, July 1998, pp. 909-930.
WWW Link. 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.
WWW Link. 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.
WWW Link. 0304
BibRef

Nanni, L.[Loris], Lumini, A.[Alessandra],
FuzzyBagging: A novel ensemble of classifiers,
PR(39), No. 3, March 2006, pp. 488-490.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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


Zhang, F.[Fei], Huang, W.J.[Wei Jie], Chan, P.P.K.[Patrick P.K.],
Hardness of evasion of multiple classifier system with non-linear classifiers,
ICWAPR14(56-60)
IEEE DOI 1402
Bagging 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
Hierarchical Combination, Multi-Stage Classifiers .


Last update:Sep 18, 2017 at 11:34:11