-
1
-
-
84919773193
-
Do we need hundreds of classifiers to solve real world classification problems?
-
M. Fernández-Delgado, E. Cernadas, S. Barro, and D. Amorim Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res. 15 1 2014 3133 3181
-
(2014)
J. Mach. Learn. Res.
, vol.15
, Issue.1
, pp. 3133-3181
-
-
Fernández-Delgado, M.1
Cernadas, E.2
Barro, S.3
Amorim, D.4
-
4
-
-
84887090067
-
A survey of multiple classifier systems as hybrid systems
-
M. Woźniak, M. Graña, and E. Corchado A survey of multiple classifier systems as hybrid systems Inform. Fusion 16 2014 3 17
-
(2014)
Inform. Fusion
, vol.16
, pp. 3-17
-
-
Woźniak, M.1
Graña, M.2
Corchado, E.3
-
5
-
-
77958064179
-
Mining data with random forests: A survey and results of new tests
-
A. Verikas, A. Gelzinis, and M. Bacauskiene Mining data with random forests: a survey and results of new tests Pattern Recogn. 44 2011 330 349
-
(2011)
Pattern Recogn.
, vol.44
, pp. 330-349
-
-
Verikas, A.1
Gelzinis, A.2
Bacauskiene, M.3
-
6
-
-
84887978175
-
Random forest classifiers: A survey and future research directions
-
V.Y. Kulkarni, and P.K. Sinha Random forest classifiers: a survey and future research directions Int. J. Adv. Comput. 36 1 2013 1144 1153
-
(2013)
Int. J. Adv. Comput.
, vol.36
, Issue.1
, pp. 1144-1153
-
-
Kulkarni, V.Y.1
Sinha, P.K.2
-
8
-
-
0000764772
-
The use of multiple measurements in taxonomic problems
-
R.A. Fisher The use of multiple measurements in taxonomic problems Ann. Eugenic. 7 2 1936 179 188 10.1111/j.1469-1809.1936.tb02137.x
-
(1936)
Ann. Eugenic.
, vol.7
, Issue.2
, pp. 179-188
-
-
Fisher, R.A.1
-
12
-
-
0003516711
-
-
Mayo Clinic, Rochester
-
T.M. Therneau, E.J. Atkinson, An Introduction to Recursive Partitioning Using the rpartRoutine, Technical Report 61, Section of Biostatistics, Mayo Clinic, Rochester, 1997. < http://www.mayo.edu/hsr/techrpt/61.pdf >.
-
(1997)
An Introduction to Recursive Partitioning Using the RpartRoutine, Technical Report 61, Section of Biostatistics
-
-
Therneau, T.M.1
Atkinson, E.J.2
-
13
-
-
84859888093
-
Are decision trees always greener on the open (source) side of the fence?
-
S.A. Moore, D.M. Daddario, J. Kurinskas, G.M. Weiss, Are decision trees always greener on the open (source) side of the fence?, in: Proceedings of DMIN, 2009, pp. 185-188.
-
(2009)
Proceedings of DMIN
, pp. 185-188
-
-
Moore, S.A.1
Daddario, D.M.2
Kurinskas, J.3
Weiss, G.M.4
-
15
-
-
0025389210
-
Boolean feature discovery in empirical learning
-
G. Pagallo, and D. Huassler Boolean feature discovery in empirical learning Mach. Learn. 5 1 1990 71 99
-
(1990)
Mach. Learn.
, vol.5
, Issue.1
, pp. 71-99
-
-
Pagallo, G.1
Huassler, D.2
-
16
-
-
0012984528
-
Lookahead and pathology in decision tree induction
-
S. Murthy, S. Salzberg, Lookahead and pathology in decision tree induction, in: IJCAI, 1995, pp. 1025-1033.
-
(1995)
IJCAI
, pp. 1025-1033
-
-
Murthy, S.1
Salzberg, S.2
-
19
-
-
84893422038
-
Survival analysis of automobile components using mutually exclusive forests
-
A. Eyal, L. Rokach, M. Kalech, O. Amir, R. Chougule, R. Vaidyanathan, and K. Pattada Survival analysis of automobile components using mutually exclusive forests IEEE Trans. Syst. Man Cybernet.: Syst. 44 2 2014 246 253
-
(2014)
IEEE Trans. Syst. Man Cybernet.: Syst.
, vol.44
, Issue.2
, pp. 246-253
-
-
Eyal, A.1
Rokach, L.2
Kalech, M.3
Amir, O.4
Chougule, R.5
Vaidyanathan, R.6
Pattada, K.7
-
22
-
-
84910677328
-
OCCT: A one-class clustering tree for implementing one-to-many data linkage
-
M. Dror, A. Shabtai, L. Rokach, and Y. Elovici OCCT: a one-class clustering tree for implementing one-to-many data linkage IEEE Trans. Knowl. Data Eng. 26 3 2014 682 697
-
(2014)
IEEE Trans. Knowl. Data Eng.
, vol.26
, Issue.3
, pp. 682-697
-
-
Dror, M.1
Shabtai, A.2
Rokach, L.3
Elovici, Y.4
-
23
-
-
84926659728
-
A Decision Tree Based Recommender System
-
A. Gershman, A. Meisels, K.H. Lüke, L. Rokach, A. Schclar, A. Sturm, A Decision Tree Based Recommender System, in: IICS, 2010, pp. 170-179.
-
(2010)
IICS
, pp. 170-179
-
-
Gershman, A.1
Meisels, A.2
Lüke, K.H.3
Rokach, L.4
Schclar, A.5
Sturm, A.6
-
24
-
-
84871713974
-
Initial profile generation in recommender systems using pairwise comparison
-
L. Rokach, and S. Kisilevich Initial profile generation in recommender systems using pairwise comparison IEEE Trans. Syst. Man Cybernet. Part C: Appl. Rev. 42 6 2012 1854 1859
-
(2012)
IEEE Trans. Syst. Man Cybernet. Part C: Appl. Rev.
, vol.42
, Issue.6
, pp. 1854-1859
-
-
Rokach, L.1
Kisilevich, S.2
-
25
-
-
57149097866
-
Ranking with decision tree
-
F. Xia, W. Zhang, F. Li, and Y. Yang Ranking with decision tree Knowl. Inform. Syst. 17 3 2008 381 395
-
(2008)
Knowl. Inform. Syst.
, vol.17
, Issue.3
, pp. 381-395
-
-
Xia, F.1
Zhang, W.2
Li, F.3
Yang, Y.4
-
26
-
-
84923442411
-
Large-scale learning to rank using boosted decision trees
-
Cambridge U. Press
-
K.M. Svore, and C.J. Burges Large-scale learning to rank using boosted decision trees Scaling Up Machine Learning 2011 Cambridge U. Press
-
(2011)
Scaling Up Machine Learning
-
-
Svore, K.M.1
Burges, C.J.2
-
29
-
-
33748611921
-
Ensemble based systems in decision making
-
R. Polikar Ensemble based systems in decision making IEEE Circ. Syst. Mag. 6 3 2006 21 45
-
(2006)
IEEE Circ. Syst. Mag.
, vol.6
, Issue.3
, pp. 21-45
-
-
Polikar, R.1
-
31
-
-
0018465664
-
Composite classifier system design: Concepts and methodology
-
B.V. Dasarathy, and B.V. Sheela Composite classifier system design: concepts and methodology Proc. IEEE 67 5 1979 708 713
-
(1979)
Proc. IEEE
, vol.67
, Issue.5
, pp. 708-713
-
-
Dasarathy, B.V.1
Sheela, B.V.2
-
32
-
-
0000173488
-
Tree-based models
-
J.M. Chambers, T.J. Hastie, Wadsworth & Brooks Pacific Grove, CA
-
L.A. Clark, and D. Pergibon Tree-based models J.M. Chambers, T.J. Hastie, Statistical Models in S 1992 Wadsworth & Brooks Pacific Grove, CA
-
(1992)
Statistical Models in S
-
-
Clark, L.A.1
Pergibon, D.2
-
34
-
-
35348915328
-
Classifier ensembles: Select real-world applications
-
N.C. Oza, and K. Tumer Classifier ensembles: select real-world applications Inform. Fusion 9 1 2008 4 20
-
(2008)
Inform. Fusion
, vol.9
, Issue.1
, pp. 4-20
-
-
Oza, N.C.1
Tumer, K.2
-
36
-
-
0004053609
-
-
Oregon State University
-
T.G. Dietterich, E.B. Kong, Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms, Tech. Rep., Oregon State University, 1995.
-
(1995)
Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms, Tech. Rep.
-
-
Dietterich, T.G.1
Kong, E.B.2
-
39
-
-
84862515469
-
A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches
-
M. Galar, A. Fernandez, E. Barrenechea, H. Bustince, and F. Herrera A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches IEEE Trans. Syst. Man Cybernet. Part C: Appl. Rev. 42 4 2012 463 484
-
(2012)
IEEE Trans. Syst. Man Cybernet. Part C: Appl. Rev.
, vol.42
, Issue.4
, pp. 463-484
-
-
Galar, M.1
Fernandez, A.2
Barrenechea, E.3
Bustince, H.4
Herrera, F.5
-
40
-
-
84881072864
-
Eusboost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling
-
M. Galar, A. Fernández, E. Barrenechea, and F. Herrera Eusboost: enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling Pattern Recogn. 46 12 2013 3460 3471
-
(2013)
Pattern Recogn.
, vol.46
, Issue.12
, pp. 3460-3471
-
-
Galar, M.1
Fernández, A.2
Barrenechea, E.3
Herrera, F.4
-
42
-
-
78149292125
-
Dynamic weighted majority: A new ensemble method for tracking concept drift
-
J.Z. Kolter, M. Maloof, Dynamic weighted majority: a new ensemble method for tracking concept drift, in: Third IEEE International Conference on Data Mining, 2003, ICDM 2003, 2003, pp. 123-130.
-
(2003)
Third IEEE International Conference on Data Mining, ICDM 2003
, pp. 123-130
-
-
Kolter, J.Z.1
Maloof, M.2
-
43
-
-
77949913486
-
The impact of diversity on online ensemble learning in the presence of concept drift
-
L.L. Minku, A.P. White, and X. Yao The impact of diversity on online ensemble learning in the presence of concept drift IEEE Trans. Knowl. Data Eng. 22 5 2010 730 742
-
(2010)
IEEE Trans. Knowl. Data Eng.
, vol.22
, Issue.5
, pp. 730-742
-
-
Minku, L.L.1
White, A.P.2
Yao, X.3
-
44
-
-
84857738059
-
DDD: A new ensemble approach for dealing with concept drift
-
L.L. Minku, and X. Yao DDD: a new ensemble approach for dealing with concept drift IEEE Trans. Knowl. Data Eng. 24 4 2012 619 633
-
(2012)
IEEE Trans. Knowl. Data Eng.
, vol.24
, Issue.4
, pp. 619-633
-
-
Minku, L.L.1
Yao, X.2
-
45
-
-
0242515926
-
Attribute bagging: Improving accuracy of classifier ensembles by using random feature subsets
-
R. Bryll, R. Gutierrez-Osuna, and F. Quek Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets Pattern Recogn. 36 6 2003 1291 1302
-
(2003)
Pattern Recogn.
, vol.36
, Issue.6
, pp. 1291-1302
-
-
Bryll, R.1
Gutierrez-Osuna, R.2
Quek, F.3
-
46
-
-
77955565395
-
Decision forest for classification of gene expression data
-
J. Huang, H. Fang, and X. Fan Decision forest for classification of gene expression data Comput. Biol. Med. 40 8 2010 698 704
-
(2010)
Comput. Biol. Med.
, vol.40
, Issue.8
, pp. 698-704
-
-
Huang, J.1
Fang, H.2
Fan, X.3
-
47
-
-
38349121661
-
Genetic algorithm-based feature set partitioning for classification problems
-
L. Rokach Genetic algorithm-based feature set partitioning for classification problems Pattern Recogn. 41 5 2008 1676 1700
-
(2008)
Pattern Recogn.
, vol.41
, Issue.5
, pp. 1676-1700
-
-
Rokach, L.1
-
48
-
-
33646426343
-
Data mining by attribute decomposition with semiconductor manufacturing case study
-
Springer US
-
O. Maimon, and L. Rokach Data mining by attribute decomposition with semiconductor manufacturing case study Data Mining for Design and Manufacturing 2001 Springer US 311 336
-
(2001)
Data Mining for Design and Manufacturing
, pp. 311-336
-
-
Maimon, O.1
Rokach, L.2
-
49
-
-
52949141834
-
Decision trees for hierarchical multi-label classification
-
C. Vens, J. Struyf, L. Schietgat, S. Džeroski, and H. Blockeel Decision trees for hierarchical multi-label classification Mach. Learn. 73 2 2008 185 214
-
(2008)
Mach. Learn.
, vol.73
, Issue.2
, pp. 185-214
-
-
Vens, C.1
Struyf, J.2
Schietgat, L.3
Džeroski, S.4
Blockeel, H.5
-
50
-
-
84876948280
-
A time series forest for classification and feature extraction
-
H. Deng, G. Runger, E. Tuv, and M. Vladimir A time series forest for classification and feature extraction Inform. Sci. 239 2013 142 153
-
(2013)
Inform. Sci.
, vol.239
, pp. 142-153
-
-
Deng, H.1
Runger, G.2
Tuv, E.3
Vladimir, M.4
-
51
-
-
0003682772
-
-
Rutgers University, Department of Computer Science, New Brunswick, NJ
-
T. Mitchell, The Need for Biases in Learning Generalizations, Technical Report CBM-TR-117, Rutgers University, Department of Computer Science, New Brunswick, NJ, 1980.
-
(1980)
The Need for Biases in Learning Generalizations, Technical Report CBM-TR-117
-
-
Mitchell, T.1
-
52
-
-
84858795085
-
The impact of diversity on the accuracy of evidential classifier ensembles
-
Y. Bi The impact of diversity on the accuracy of evidential classifier ensembles Int. J. Approx. Reason. 53 4 2012 584 607
-
(2012)
Int. J. Approx. Reason.
, vol.53
, Issue.4
, pp. 584-607
-
-
Bi, Y.1
-
53
-
-
0003503113
-
On the link between error correlation and error reduction in decision tree ensembles
-
University of California, Irvine
-
K.M. Ali, M.J. Pazzani, On the link between error correlation and error reduction in decision tree ensembles, Information and Computer Science, University of California, Irvine, 1995, pp. 95-38.
-
(1995)
Information and Computer Science
, pp. 95-38
-
-
Ali, K.M.1
Pazzani, M.J.2
-
56
-
-
84855493529
-
Parameter determination and feature selection for C4.5 algorithm using scatter search approach
-
S.W. Lin, and S.C. Chen Parameter determination and feature selection for C4.5 algorithm using scatter search approach Soft Comput. 16 1 2012 63 75
-
(2012)
Soft Comput.
, vol.16
, Issue.1
, pp. 63-75
-
-
Lin, S.W.1
Chen, S.C.2
-
58
-
-
79958861311
-
Feature-subspace aggregating: Ensembles for stable and unstable learners
-
K.M. Ting, J.R. Wells, S.C. Tan, S.W. Teng, and G.I. Webb Feature-subspace aggregating: ensembles for stable and unstable learners Mach. Learn. 82 3 2011 375 397
-
(2011)
Mach. Learn.
, vol.82
, Issue.3
, pp. 375-397
-
-
Ting, K.M.1
Wells, J.R.2
Tan, S.C.3
Teng, S.W.4
Webb, G.I.5
-
59
-
-
70449630542
-
Decision tree ensemble: Small heterogeneous is better than large homogeneous
-
M. Gashler, C. Giraud-Carrier, T. Martinez, Decision tree ensemble: small heterogeneous is better than large homogeneous, in: IEEE Seventh International Conference on Machine Learning and Applications, 2008, ICMLA'08, 2008, pp. 900-905.
-
(2008)
IEEE Seventh International Conference on Machine Learning and Applications, ICMLA'08
, pp. 900-905
-
-
Gashler, M.1
Giraud-Carrier, C.2
Martinez, T.3
-
60
-
-
44449124996
-
RotBoost: A technique for combining Rotation Forest and AdaBoost
-
C.X. Zhang, and J.S. Zhang RotBoost: a technique for combining Rotation Forest and AdaBoost Pattern Recogn. Lett. 29 10 2008 1524 1536
-
(2008)
Pattern Recogn. Lett.
, vol.29
, Issue.10
, pp. 1524-1536
-
-
Zhang, C.X.1
Zhang, J.S.2
-
62
-
-
77950296222
-
Semi-supervised learning for tree-structured ensembles of RBF networks with co-training
-
M.F.A. Hady, F. Schwenker, and G. Palm Semi-supervised learning for tree-structured ensembles of RBF networks with co-training Neural Networks 23 4 2010 497 509
-
(2010)
Neural Networks
, vol.23
, Issue.4
, pp. 497-509
-
-
Hady, M.F.A.1
Schwenker, F.2
Palm, G.3
-
64
-
-
0030356238
-
Actively searching for an effective neural network ensemble
-
D.W. Opitz, and J.W. Shavlik Actively searching for an effective neural network ensemble Connect. Sci. 8 3-4 1996 337 354
-
(1996)
Connect. Sci.
, vol.8
, Issue.3-4
, pp. 337-354
-
-
Opitz, D.W.1
Shavlik, J.W.2
-
65
-
-
0003637516
-
-
School of Computing Science, University of Technology, Sydney. Australia
-
W. Buntine, A Theory of Learning Classification Rules, Doctoral Dissertation, School of Computing Science, University of Technology, Sydney. Australia, 1990.
-
(1990)
A Theory of Learning Classification Rules, Doctoral Dissertation
-
-
Buntine, W.1
-
66
-
-
0033281701
-
Improved boosting algorithms including confidence-rated predictions
-
R.E. Shapire, and Y. Singer Improved boosting algorithms including confidence-rated predictions Mach. Learn. 37 1999 297 336
-
(1999)
Mach. Learn.
, vol.37
, pp. 297-336
-
-
Shapire, R.E.1
Singer, Y.2
-
69
-
-
70349750474
-
Troika-an improved stacking schema for classification tasks
-
E. Menahem, L. Rokach, and Y. Elovici Troika-an improved stacking schema for classification tasks Inform. Sci. 179 24 2009 4097 4122
-
(2009)
Inform. Sci.
, vol.179
, Issue.24
, pp. 4097-4122
-
-
Menahem, E.1
Rokach, L.2
Elovici, Y.3
-
70
-
-
84933552675
-
-
Grading Classifiers, Austrian Research Institute for Artificial intelligence
-
A.K. Seewald, J. Faurnkranz, Grading Classifiers, Austrian Research Institute for Artificial intelligence, 2001.
-
(2001)
-
-
Seewald, A.K.1
Faurnkranz, J.2
-
72
-
-
0030211964
-
Bagging predictors
-
L. Breiman Bagging predictors Mach. Learn. 24 2 1996 123 140
-
(1996)
Mach. Learn.
, vol.24
, Issue.2
, pp. 123-140
-
-
Breiman, L.1
-
73
-
-
0035478854
-
Random forests
-
L. Breiman Random forests Mach. Learn. 45 1 2001 5 32
-
(2001)
Mach. Learn.
, vol.45
, Issue.1
, pp. 5-32
-
-
Breiman, L.1
-
76
-
-
0034250160
-
An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
-
T.G. Dietterich An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization Mach. Learn. 40 2 2000 139 157
-
(2000)
Mach. Learn.
, vol.40
, Issue.2
, pp. 139-157
-
-
Dietterich, T.G.1
-
77
-
-
77953178544
-
On-line random forests
-
A. Saffari, C. Leistner, J. Santner, M. Godec, H. Bischof, On-line random forests, in: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), 2009, pp. 1393-1400.
-
(2009)
IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops)
, pp. 1393-1400
-
-
Saffari, A.1
Leistner, C.2
Santner, J.3
Godec, M.4
Bischof, H.5
-
78
-
-
0032139235
-
The random subspace method for constructing decision forests
-
T.K. Ho The random subspace method for constructing decision forests IEEE Trans. Pattern Anal. Mach. Intell. 20 8 1998 832 844
-
(1998)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.20
, Issue.8
, pp. 832-844
-
-
Ho, T.K.1
-
79
-
-
62649121782
-
Creating ensembles of decision trees through sampling
-
Costa Mesa, CA, June
-
C. Kamath, E. Cantu-Paz, Creating ensembles of decision trees through sampling, in: Proceedings, 33-rd Symposium on the Interface of Computing Science and Statistics, Costa Mesa, CA, June 2001.
-
(2001)
Proceedings, 33-rd Symposium on the Interface of Computing Science and Statistics
-
-
Kamath, C.1
Cantu-Paz, E.2
-
81
-
-
84881077165
-
One class random forests
-
C. Désir, S. Bernard, C. Petitjean, and L. Heutte One class random forests Pattern Recogn. 46 12 2013 3490 3506
-
(2013)
Pattern Recogn.
, vol.46
, Issue.12
, pp. 3490-3506
-
-
Désir, C.1
Bernard, S.2
Petitjean, C.3
Heutte, L.4
-
83
-
-
65949106613
-
Random projection ensemble classifiers
-
Springer Berlin, Heidelberg
-
A. Schclar, and L. Rokach Random projection ensemble classifiers Enterprise Information Systems 2009 Springer Berlin, Heidelberg 309 316
-
(2009)
Enterprise Information Systems
, pp. 309-316
-
-
Schclar, A.1
Rokach, L.2
-
84
-
-
0035470889
-
Greedy function approximation: A gradient boosting machine
-
J.H. Friedman Greedy function approximation: a gradient boosting machine Ann. Stat. 2001 1189 1232
-
(2001)
Ann. Stat.
, pp. 1189-1232
-
-
Friedman, J.H.1
-
87
-
-
0034276320
-
Randomizing outputs to increase prediction accuracy
-
L. Breiman Randomizing outputs to increase prediction accuracy Mach. Learn. 40 3 2000 229 242
-
(2000)
Mach. Learn.
, vol.40
, Issue.3
, pp. 229-242
-
-
Breiman, L.1
-
88
-
-
22844439516
-
Switching class labels to generate classification ensembles
-
G. Martínez-Muñoz, and A. Suárez Switching class labels to generate classification ensembles Pattern Recogn. 38 10 2005 1483 1494
-
(2005)
Pattern Recogn.
, vol.38
, Issue.10
, pp. 1483-1494
-
-
Martínez-Muñoz, G.1
Suárez, A.2
-
89
-
-
0001931577
-
An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
-
E. Bauer, and R. Kohavi An empirical comparison of voting classification algorithms: bagging, boosting, and variants Mach. Learn. 35 1999 1 38
-
(1999)
Mach. Learn.
, vol.35
, pp. 1-38
-
-
Bauer, E.1
Kohavi, R.2
-
90
-
-
0000551189
-
Popular ensemble methods: An empirical study
-
D. Opitz, and R. Maclin Popular ensemble methods: an empirical study J. Artif. Res. 11 1999 169 198
-
(1999)
J. Artif. Res.
, vol.11
, pp. 169-198
-
-
Opitz, D.1
Maclin, R.2
-
92
-
-
33947231519
-
A comparison of decision tree ensemble creation techniques
-
R.E. Banfield, L.O. Hall, K.W. Bowyer, and W.P. Kegelmeyer A comparison of decision tree ensemble creation techniques IEEE Trans. Pattern Anal. Mach. Intell. 29 1 2007 173 180
-
(2007)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.29
, Issue.1
, pp. 173-180
-
-
Banfield, R.E.1
Hall, L.O.2
Bowyer, K.W.3
Kegelmeyer, W.P.4
-
93
-
-
0002139432
-
SPRINT: A scalable parallel classifier for data mining
-
J. Shafer, R. Agrawal, M. Mehta, SPRINT: a scalable parallel classifier for data mining, in: Proceedings of 1996 International Conference Very Large Data Bases, 1996, pp. 544-555.
-
(1996)
Proceedings of 1996 International Conference Very Large Data Bases
, pp. 544-555
-
-
Shafer, J.1
Agrawal, R.2
Mehta, M.3
-
95
-
-
38049019831
-
-
Fast C4.5
-
P. He, L. Chen, X.H. Xu, Fast C4.5, in: 2007 International Conference on IEEE Machine Learning and Cybernetics, vol. 5, 2007, pp. 2841-2846.
-
(2007)
International Conference on IEEE Machine Learning and Cybernetics
, vol.5
, pp. 2841-2846
-
-
He, P.1
Chen, L.2
Xu, X.H.3
-
96
-
-
77955032649
-
Planet: Massively parallel learning of tree ensembles with mapreduce
-
B. Panda, J.S. Herbach, S. Basu, and R.J. Bayardo Planet: massively parallel learning of tree ensembles with mapreduce Proc. VLDB Endowment 2 2 2009 1426 1437
-
(2009)
Proc. VLDB Endowment
, vol.2
, Issue.2
, pp. 1426-1437
-
-
Panda, B.1
Herbach, J.S.2
Basu, S.3
Bayardo, R.J.4
-
97
-
-
74549138522
-
Stochastic gradient boosted distributed decision trees
-
J. Ye, J.H. Chow, J. Chen, Z. Zheng, Stochastic gradient boosted distributed decision trees, in: Proceedings of the 18th ACM Conference on Information and Knowledge Management, 2009, pp. 2061-2064.
-
(2009)
Proceedings of the 18th ACM Conference on Information and Knowledge Management
, pp. 2061-2064
-
-
Ye, J.1
Chow, J.H.2
Chen, J.3
Zheng, Z.4
-
98
-
-
84865330735
-
Scalable and parallel boosting with mapreduce
-
I. Palit, and C.K. Reddy Scalable and parallel boosting with mapreduce IEEE Trans. Knowl. Data Eng. 24 10 2012 1904 1916
-
(2012)
IEEE Trans. Knowl. Data Eng.
, vol.24
, Issue.10
, pp. 1904-1916
-
-
Palit, I.1
Reddy, C.K.2
-
99
-
-
84906873734
-
On the use of MapReduce for imbalanced big data using Random Forest
-
S. del Río, V. López, J.M. Benítez, and F. Herrera On the use of MapReduce for imbalanced big data using Random Forest Inform. Sci. 285 2014 112 137
-
(2014)
Inform. Sci.
, vol.285
, pp. 112-137
-
-
Del Río, S.1
López, V.2
Benítez, J.M.3
Herrera, F.4
-
103
-
-
0036567392
-
Ensembling neural networks: Many could be better than all
-
Z.H. Zhou, J. Wu, and W. Tang Ensembling neural networks: many could be better than all Artif. Intell. 137 2002 239 263
-
(2002)
Artif. Intell.
, vol.137
, pp. 239-263
-
-
Zhou, Z.H.1
Wu, J.2
Tang, W.3
-
105
-
-
84856600382
-
Efficient prediction algorithms for binary decomposition techniques
-
S.H. Park, and J. Furnkranz Efficient prediction algorithms for binary decomposition techniques Data Min. Knowl. Disc. 2012 1 38
-
(2012)
Data Min. Knowl. Disc.
, pp. 1-38
-
-
Park, S.H.1
Furnkranz, J.2
-
107
-
-
33749318929
-
-
Columbia University
-
A.L. Prodromidis, S.J. Stolfo, P.K. Chan, Effective and Efficient Pruning of Metaclassifiers in a Distributed Data Mining System, Technical Report CUCS-017-99, Columbia University, 1999.
-
(1999)
Effective and Efficient Pruning of Metaclassifiers in A Distributed Data Mining System, Technical Report CUCS-017-99
-
-
Prodromidis, A.L.1
Stolfo, S.J.2
Chan, P.K.3
-
108
-
-
14344255621
-
Ensemble selection from libraries of models
-
July 04-08, 2004, Banff, Alberta, Canada
-
R. Caruana, A. Niculescu-Mizil, G. Crew, A. Ksikes, Ensemble selection from libraries of models, in: Twenty-First International Conference on Machine Learning, July 04-08, 2004, Banff, Alberta, Canada, 2004.
-
(2004)
Twenty-First International Conference on Machine Learning
-
-
Caruana, R.1
Niculescu-Mizil, A.2
Crew, G.3
Ksikes, A.4
-
109
-
-
34547654182
-
EROS: Ensemble rough subspaces
-
Q. Hu, D. Yu, Z. Xie, and X. Li EROS: ensemble rough subspaces Pattern Recogn. 40 2007 3728 3739
-
(2007)
Pattern Recogn.
, vol.40
, pp. 3728-3739
-
-
Hu, Q.1
Yu, D.2
Xie, Z.3
Li, X.4
-
110
-
-
10444241978
-
Ensemble diversity measures and their application to thinning
-
R.E. Banfield, L.O. Hall, K.W. Bowyer, and W.P. Kegelmeyer Ensemble diversity measures and their application to thinning Inform. Fusion 6 1 2005 49 62
-
(2005)
Inform. Fusion
, vol.6
, Issue.1
, pp. 49-62
-
-
Banfield, R.E.1
Hall, L.O.2
Bowyer, K.W.3
Kegelmeyer, W.P.4
-
111
-
-
78049528785
-
An ensemble uncertainty aware measure for directed hill climbing ensemble pruning
-
I. Partalas, G. Tsoumakas, and I. Vlahavas An ensemble uncertainty aware measure for directed hill climbing ensemble pruning Mach. Learn. 81 3 2010 257 282
-
(2010)
Mach. Learn.
, vol.81
, Issue.3
, pp. 257-282
-
-
Partalas, I.1
Tsoumakas, G.2
Vlahavas, I.3
-
112
-
-
58549093526
-
Collective-agreement-based pruning of ensembles
-
L. Rokach Collective-agreement-based pruning of ensembles Comput. Stat. Data Anal. 53 4 2009 1015 1026
-
(2009)
Comput. Stat. Data Anal.
, vol.53
, Issue.4
, pp. 1015-1026
-
-
Rokach, L.1
-
113
-
-
84863447426
-
Search for the smallest random forest
-
H. Zhang, and M. Wang Search for the smallest random forest Stat. Interface 2 2009 381 388
-
(2009)
Stat. Interface
, vol.2
, pp. 381-388
-
-
Zhang, H.1
Wang, M.2
-
114
-
-
8344279588
-
Selective Ensemble of Decision Trees
-
Guoyin Wang, Qing Liu, Yiyu Yao, Andrzej Skowron (Eds.), Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing
-
Z.H. Zhou, W. Tang, Selective ensemble of decision trees, in: Guoyin Wang, Qing Liu, Yiyu Yao, Andrzej Skowron (Eds.), Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, 9th International Conference, RSFDGrC, Chongqing, China, Proceedings, Lecture Notes in Computer Science, vol. 2639, 2003, pp. 476-483.
-
(2003)
9th International Conference, RSFDGrC, Chongqing, China, Proceedings, Lecture Notes in Computer Science
, vol.2639
, pp. 476-483
-
-
Zhou, Z.H.1
Tang, W.2
-
115
-
-
33646887846
-
-
Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, Chongqing, China
-
Guoyin Wang, Qing Liu, Yiyu Yao, Andrzej Skowron (Eds.), Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, 9th International Conference, RSFDGrC, Chongqing, China, Proceedings, Lecture Notes in Computer Science, vol. 2639, 2003, pp. 476-483.
-
(2003)
9th International Conference, RSFDGrC, Proceedings, Lecture Notes in Computer Science
, vol.2639
, pp. 476-483
-
-
Wang, G.1
Liu, Q.2
Yao, Y.3
Skowron, A.4
-
117
-
-
0012467735
-
Cost complexity-based pruning of ensemble classifiers
-
A.L. Prodromidis, and S.J. Stolfo Cost complexity-based pruning of ensemble classifiers Knowl. Inform. Syst. 3 4 2001 449 469
-
(2001)
Knowl. Inform. Syst.
, vol.3
, Issue.4
, pp. 449-469
-
-
Prodromidis, A.L.1
Stolfo, S.J.2
-
118
-
-
58549098738
-
-
T. Windeatt, G. Ardeshir, An Empirical Comparison of Pruning Methods for Ensemble Classifiers, IDA2001, LNCS, vol. 2189, 2001, pp. 208-217.
-
(2001)
An Empirical Comparison of Pruning Methods for Ensemble Classifiers, IDA2001, LNCS
, vol.2189
, pp. 208-217
-
-
Windeatt, T.1
Ardeshir, G.2
-
119
-
-
33745794076
-
Ensemble pruning via semi-definite programming
-
Y. Zhang, S. Burer, and W.N. Street Ensemble pruning via semi-definite programming J. Mach. Learn. Res. 7 2006 1315 1338
-
(2006)
J. Mach. Learn. Res.
, vol.7
, pp. 1315-1338
-
-
Zhang, Y.1
Burer, S.2
Street, W.N.3
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