-
1
-
-
84871211556
-
Content management system effort estimation using bagging predictors
-
M. Iskander, V. Kapila, and M. Karim, Eds
-
Aggarwal, N., Prakash, N., and Sofat, S. 2010. Content Management System Effort Estimation Using Bagging Predictors. in Proceedings of the International Joint Conference on Computer Information Systems Sciences and Engineering Technological Developments in Education and Automation, M. Iskander, V. Kapila, and M. Karim, Eds. 19-24.
-
(2010)
Proceedings of the International Joint Conference on Computer Information Systems Sciences and Engineering Technological Developments in Education and Automation
, pp. 19-24
-
-
Aggarwal, N.1
Prakash, N.2
Sofat, S.3
-
2
-
-
0003368229
-
A comparative evaluation of sequential feature selection algorithms
-
D. Fisher and H.-J. Lenz, Eds. Springer, Chapter 4
-
Aha, D. W. and Bankert, R. L. 1996. A Comparative Evaluation of Sequential Feature Selection Algorithms. in Learning From Data, D. Fisher and H.-J. Lenz, Eds. Springer, Chapter 4, 199-206.
-
(1996)
Learning From Data
, pp. 199-206
-
-
Aha, D.W.1
Bankert, R.L.2
-
4
-
-
33845667087
-
Feature subset selection using ant colony optimization
-
Al-Ani, A. 2005. Feature Subset Selection Using Ant Colony Optimization. Int. J.Comput. Intell. 2, 1, 53-58.
-
(2005)
Int. J.Comput. Intell.
, vol.2
, Issue.1
, pp. 53-58
-
-
Al-Ani, A.1
-
5
-
-
0033556775
-
Boosting regression estimators
-
Avnimelech, R. and Intrator, N. 1999. Boosting Regression Estimators. Neural Comput. 11, 499-520.
-
(1999)
Neural Comput.
, vol.11
, pp. 499-520
-
-
Avnimelech, R.1
Intrator, N.2
-
7
-
-
77953811058
-
Ensembles of jittered association rule classifiers
-
Azevedo, P. J. and Jorge, A. M. 2010. Ensembles of Jittered Association Rule Classifiers. Data Min. Knowl. Discov. 21, 1, 91-129.
-
(2010)
Data Min. Knowl. Discov.
, vol.21
, Issue.1
, pp. 91-129
-
-
Azevedo, P.J.1
Jorge, A.M.2
-
8
-
-
0037337904
-
Clustering ensembles of neural network models
-
Bakker, B. and Heskes, T. 2003. Clustering Ensembles of Neural Network Models. Neural Netw. 16, 2, 261- 269.
-
(2003)
Neural Netw.
, vol.16
, Issue.2
, pp. 261-269
-
-
Bakker, B.1
Heskes, T.2
-
9
-
-
0016557674
-
Multidimensional binary search trees used for associative searching
-
Bentley, J. L. 1975. Multidimensional Binary Search Trees Used For Associative Searching.Comm. Acm 18, 9, 509-517.
-
(1975)
Comm. Acm
, vol.18
, Issue.9
, pp. 509-517
-
-
Bentley, J.L.1
-
10
-
-
26944492591
-
Adaptive radius immune algorithm for data clustering
-
Springer
-
Bezerra, G. B., Barra, T. V., Castro, L. N., and Von Zuben, F. J. 2005. Adaptive Radius Immune Algorithm For Data Clustering. in Proceedings of the International Conference on Artificial Immune Systems (Icaris'05). Lecture Notes in Computer Science, Vol. 3627. Springer, 290-303.
-
(2005)
Proceedings of the International Conference on Artificial Immune Systems (Icaris'05). Lecture Notes in Computer Science
, vol.3627
, pp. 290-303
-
-
Bezerra, G.B.1
Barra, T.V.2
Castro, L.N.3
Van Zuben, F.J.4
-
11
-
-
70350700681
-
New ensemble methods for evolving data streams
-
Acm, New York
-
Bifet, A., Holmes, G., Pfahringer, B., Kirkby, R., and Gavald́A, R. 2009. New Ensemble Methods For Evolving Data Streams. in Proceedings of the Annual Acm Sigkdd Conference on Knowledge Discovery and Data Mining (Kdd'09). Acm, New York, 139-148.
-
(2009)
Proceedings of the Annual Acm Sigkdd Conference on Knowledge Discovery and Data Mining (Kdd'09)
, pp. 139-148
-
-
Bifet, A.1
Holmes, G.2
Pfahringer, B.3
Kirkby, R.4
Gavald́A, R.5
-
13
-
-
0037186681
-
Improving nonparametric regression methods by bagging and boosting
-
Borra, S. and Ciaccio, A. D. 2002. Improving Nonparametric Regression Methods By Bagging and Boosting.Comput. Statist. Data Anal. 38, 4, 407-420.
-
(2002)
Comput. Statist. Data Anal.
, vol.38
, Issue.4
, pp. 407-420
-
-
Borra, S.1
Ciaccio, A.D.2
-
14
-
-
60649115993
-
-
Springer
-
Brazdil, P., Giraud-Carrier, C., Soares, C., and Vilalta, R. 2009. Metalearning: Applications To Data Mining. Springer.
-
(2009)
Metalearning: Applications To Data Mining
-
-
Brazdil, P.1
Giraud-Carrier, C.2
Soares, C.3
Vilalta, R.4
-
15
-
-
0030211964
-
Bagging predictors
-
Breiman, L. 1996A. Bagging Predictors. Mach. Learn. 26, 123-140.
-
(1996)
Mach. Learn.
, vol.26
, pp. 123-140
-
-
Breiman, L.1
-
16
-
-
0030344230
-
Heuristics of instability and stabilization in model selection
-
Breiman, L. 1996B. Heuristics of Instability and Stabilization in Model Selection. Ann. Statist. 24, 6, 2350-2383.
-
(1996)
Ann. Statist.
, vol.24
, Issue.6
, pp. 2350-2383
-
-
Breiman, L.1
-
17
-
-
0030196364
-
Stacked regressions
-
Breiman, L. 1996C. Stacked Regressions. Mach. Learn. 24, 49-64.
-
(1996)
Mach. Learn.
, vol.24
, pp. 49-64
-
-
Breiman, L.1
-
18
-
-
0034276320
-
Randomizing outputs to increase prediction accuracy
-
Breiman, L. 2000. Randomizing Outputs To Increase Prediction Accuracy. Mach. Learn. 40, 3, 229-242.
-
(2000)
Mach. Learn.
, vol.40
, Issue.3
, pp. 229-242
-
-
Breiman, L.1
-
19
-
-
0035478854
-
Random forests
-
Breiman, L. 2001A. Random Forests. Mach. Learn. 45, 5-32.
-
(2001)
Mach. Learn.
, vol.45
, pp. 5-32
-
-
Breiman, L.1
-
20
-
-
0035575477
-
Using iterated bagging to debias regressions
-
Breiman, L. 2001B. Using Iterated Bagging To Debias Regressions. Mach. Learn. 45, 3, 261-277.
-
(2001)
Mach. Learn.
, vol.45
, Issue.3
, pp. 261-277
-
-
Breiman, L.1
-
22
-
-
10444221886
-
Diversity creation methods: A survey and categorisation
-
Brown, G., Wyatt, J. L., Harris, R., and Yao, X. 2005A. Diversity Creation Methods: A Survey and Categorisation. Inf. Fusion 6, 5-20.
-
(2005)
Inf. Fusion
, vol.6
, pp. 5-20
-
-
Brown, G.1
Wyatt, J.L.2
Harris, R.3
Yao, X.4
-
23
-
-
25444484657
-
Managing diversity in regression ensembles
-
Brown, G., Wyatt, J. L., and Tino, P. 2005B. Managing Diversity in Regression Ensembles. J. Mach. Learn. Res. 6, 1621-1650.
-
(2005)
J. Mach. Learn. Res.
, vol.6
, pp. 1621-1650
-
-
Brown, G.1
Wyatt, J.L.2
Tino, P.3
-
25
-
-
33746171809
-
Observations on bagging
-
Buja, A. and Stuetzle, W. 2006. Observations on Bagging. Statistica Sinica 16, 323-351.
-
(2006)
Statistica Sinica
, vol.16
, pp. 323-351
-
-
Buja, A.1
Stuetzle, W.2
-
27
-
-
14344255621
-
Ensemble selection from libraries of models
-
Caruana, R.,Niculescu-Mozil, A., Crew, G., and Ksikes, A. 2004. Ensemble Selection From Libraries of Models. in International Conference on Machine Learning.
-
(2004)
International Conference on Machine Learning
-
-
Caruana, R.1
Niculescu-Mozil, A.2
Crew, G.3
Ksikes, A.4
-
29
-
-
84871223038
-
-
Delve. Delve: Data For Evaluating Learning in Valid Experiments
-
Delve. 2002. Delve: Data For Evaluating Learning in Valid Experiments. http://www.Cs.Toronto.Edu/Delve/
-
(2002)
-
-
-
30
-
-
35048842229
-
Dynamic classifier selection by adaptive k-nearest neighbourhood rule
-
Springer
-
Didaci. L. and Giacinto, G. 2004. Dynamic Classifier Selection By Adaptive K-Nearest Neighbourhood Rule. in Proceedings of the International Workshop on Multiple Classifier Systems, F. Roli, J. Kittler, and T. Windeatt, Eds. Lecture Notes in Computer Science, Vol. 3077. Springer, 174-183.
-
(2004)
Proceedings of the International Workshop on Multiple Classifier Systems, F. Roli, J. Kittler, and T. Windeatt, Eds. Lecture Notes in Computer Science
, vol.3077
, pp. 174-183
-
-
Didaci, L.1
Giacinto, G.2
-
31
-
-
0031361611
-
Machine-learning research: Four current directions
-
Dietterich, T. G. 1997. Machine-Learning Research: Four Current Directions. Ai Mag. 18, 4, 97-136.
-
(1997)
Ai Mag.
, vol.18
, Issue.4
, pp. 97-136
-
-
Dietterich, T.G.1
-
35
-
-
0036568038
-
Boosting methods for regression
-
Duffy, N. and Helmbold, D. 2002. Boosting Methods For Regression. Mach. Learn. 47, 153-200.
-
(2002)
Mach. Learn.
, vol.47
, pp. 153-200
-
-
Duffy, N.1
Helmbold, D.2
-
36
-
-
70449344715
-
An anticorrelation kernel for subsystem training in multiple classifier systems
-
Ferrer, L., Sǒnmez, K., and Shriberg, E. 2009. An Anticorrelation Kernel For Subsystem Training in Multiple Classifier Systems. J. Mach. Learn. Res. 10, 2079-2114.
-
(2009)
J. Mach. Learn. Res.
, vol.10
, pp. 2079-2114
-
-
Ferrer, L.1
Sǒnmez, K.2
Shriberg, E.3
-
41
-
-
0031211090
-
A decision-theoretic generalization of on-line learning and an application to boosting
-
Freund, Y. and Schapire, R. E. 1997. A Decision-theoretic Generalization of on-Line Learning and An Application To Boosting. J.Comput. Syst. Sci. 55, 119-139.
-
(1997)
J.Comput. Syst. Sci.
, vol.55
, pp. 119-139
-
-
Freund, Y.1
Schapire, R.E.2
-
42
-
-
0002432565
-
Multivariate adaptive regression splines
-
Friedman, J. H. 1991. Multivariate Adaptive Regression Splines. the Ann. Statist. 19, 1, 1-141.
-
(1991)
The Ann. Statist.
, vol.19
, Issue.1
, pp. 1-141
-
-
Friedman, J.H.1
-
44
-
-
0035470889
-
Greedy function approximation: A gradient boosting machine
-
Friedman, J. H. 2001. Greedy Function Approximation: A Gradient Boosting Machine. Ann. Statist. 29, 5, 1189-1232.
-
(2001)
Ann. Statist.
, vol.29
, Issue.5
, pp. 1189-1232
-
-
Friedman, J.H.1
-
45
-
-
0037186544
-
Stochastic gradient boosting
-
Friedman, J. H. 2002. Stochastic Gradient Boosting.Comput. Statist. Data Anal. 38, 4, 367-378.
-
(2002)
Comput. Statist. Data Anal.
, vol.38
, Issue.4
, pp. 367-378
-
-
Friedman, J.H.1
-
47
-
-
21044454599
-
Cooperative coevolution of artificial neural network ensembles for pattern classification
-
Garćia-Pedrajas, N., Herv́As-Mart́inez, C., and Ortiz-Boyer, D. 2005. Cooperative Coevolution of Artificial Neural Network Ensembles For Pattern Classification. Ieee Trans. Evolut.Comput. 9, 3, 271-302.
-
(2005)
IEEE Trans. Evolut.Comput.
, vol.9
, Issue.3
, pp. 271-302
-
-
Garćia-Pedrajas, N.1
Herv́As-Mart́inez, C.2
Ortiz-Boyer, D.3
-
48
-
-
0001942829
-
Neural networks and the bias/variance dilemma
-
Geman, S., Bienenstock, E., and Doursat, R. 1992. Neural Networks and the Bias/Variance Dilemma. Neural Comput. 4, 1, 1-58.
-
(1992)
Neural Comput.
, vol.4
, Issue.1
, pp. 1-58
-
-
Geman, S.1
Bienenstock, E.2
Doursat, R.3
-
50
-
-
14544297375
-
Neural network ensembles: Evaluation of aggregation algorithms
-
Granitto, P., Verdes, P., and Ceccatto, H. 2005. Neural Network Ensembles: Evaluation of Aggregation Algorithms. Artif. Intell. 163, 2, 139-162.
-
(2005)
Artif. Intell.
, vol.163
, Issue.2
, pp. 139-162
-
-
Granitto, P.1
Verdes, P.2
Ceccatto, H.3
-
54
-
-
0003684449
-
-
Springer Series in Statistics. Springer
-
Hastie, T., Tibshirani, R., and Friedman, J. H. 2001. the Elements of Statistical Learning: Data Mining, Inference, and Predictions. Springer Series in Statistics. Springer.
-
(2001)
The Elements of Statistical Learning: Data Mining, Inference, and Predictions
-
-
Hastie, T.1
Tibshirani, R.2
Friedman, J.H.3
-
56
-
-
0032139235
-
The random subspace method for constructing decision forests
-
Ho, T. K. 1998. the Random Subspace Method For Constructing Decision Forests. Ieee Trans. Pattern Anal. Mach. Intell. 20, 8, 832-844.
-
(1998)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.20
, Issue.8
, pp. 832-844
-
-
Ho, T.K.1
-
57
-
-
0042525838
-
A constructive algorithm for training cooperative neural network ensembles
-
Islam, M. M., Yao, X., and Murase, K. 2003. A Constructive Algorithm For Training Cooperative Neural Network Ensembles. Ieee Trans. Neural Netw. 14, 4, 820-834.
-
(2003)
IEEE Trans. Neural Netw.
, vol.14
, Issue.4
, pp. 820-834
-
-
Islam, M.M.1
Yao, X.2
Murase, K.3
-
58
-
-
0031078007
-
Feature selection: Evaluation, application, and small sample performance
-
Jain, A. and Zongker, D. 1997. Feature Selection: Evaluation, Application, and Small Sample Performance. Ieee Trans. Pattern Anal. Mach. Intell. 19, 2, 153-158.
-
(1997)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.19
, Issue.2
, pp. 153-158
-
-
Jain, A.1
Zongker, D.2
-
59
-
-
33745317786
-
An experiment with association rules and classification: Post-bagging and conviction
-
Springer
-
Jorge, A. M. and Azevedo, P. J. 2005. An Experiment With Association Rules and Classification: Post-Bagging and Conviction. in Discovery Science. Lecture Notes in Computer Science, Vol. 3735. Springer, 137-149.
-
(2005)
Discovery Science. Lecture Notes in Computer Science
, vol.3735
, pp. 137-149
-
-
Jorge, A.M.1
Azevedo, P.J.2
-
60
-
-
0142025124
-
Constructing support vector machine ensemble
-
Kim, H.-C., Pang, S., Je, H.-M., Kim, D., and Bang, S.-Y. 2003. Constructing Support Vector Machine Ensemble. Pattern Recogn. 36, 12, 2757-2767.
-
(2003)
Pattern Recogn.
, vol.36
, Issue.12
, pp. 2757-2767
-
-
Kim, H.-C.1
Pang, S.2
Je, H.-M.3
Kim, D.4
Bang, S.-Y.5
-
61
-
-
0008815681
-
Exponentiated gradient versus gradient descent for linear predictors
-
Kivinen, J. and Warmuth, M. K. 1997. Exponentiated Gradient Versus Gradient Descent For Linear Predictors. Inf.Comput. 132, 1, 1-63.
-
(1997)
Inf.Comput.
, vol.132
, Issue.1
, pp. 1-63
-
-
Kivinen, J.1
Warmuth, M.K.2
-
62
-
-
38349135448
-
From dynamic classifier selection to dynamic ensemble selection
-
Ko, A. H.-R., Sabourin, R., and Britto Jr., A. D. S. 2008. From Dynamic Classifier Selection To Dynamic Ensemble Selection. Pattern Recogn. 41, 1718-1731.
-
(2008)
Pattern Recogn.
, vol.41
, pp. 1718-1731
-
-
Ko, A.H.-R.1
Sabourin, R.2
Britto Jr., S.A.D.3
-
64
-
-
37749050180
-
Dynamic weighted majority: An ensemble method for drifting concepts
-
Kolter, J. Z. andmaloof, M. A. 2007. Dynamic Weighted Majority: An Ensemble Method For Drifting Concepts. J. Mach. Learn. Res. 8, 2755-2790.
-
(2007)
J. Mach. Learn. Res.
, vol.8
, pp. 2755-2790
-
-
Kolter, J.Z.1
Maloof, M.A.2
-
66
-
-
85054435084
-
Neural network ensembles, cross validation, and active learning
-
Krogh, A. and Vedelsby, J. 1995. Neural Network Ensembles, Cross Validation, and Active Learning. Adv. Neural Inf. Process. Syst. 7, 231-238.
-
(1995)
Adv. Neural Inf. Process. Syst.
, vol.7
, pp. 231-238
-
-
Krogh, A.1
Vedelsby, J.2
-
67
-
-
0036532571
-
Switching between selection and fusion in combining classifiers: An experiment
-
Kuncheva, L. I. 2002. Switching Between Selection and Fusion in Combining Classifiers: An Experiment. Ieee Trans. Syst. Man Cybernet. B32, 2, 146-156.
-
(2002)
IEEE Trans. Syst. Man Cybernet.
, vol.B32
, Issue.2
, pp. 146-156
-
-
Kuncheva, L.I.1
-
69
-
-
70450217356
-
A multi-agent system to assist with real estate appraisals using bagging ensembles
-
Springer
-
Lasota, T., Telec, Z., Trawinski, B., and Trawinsky, K. 2009. A Multi-Agent System To Assist With Real Estate Appraisals Using Bagging Ensembles. in Computational Collective Intelligence: Semantic Web, Social Networks and Multiagent Systems, N. Nguyen, R. Kowalczyk, and S. Chen, Eds. Lecture Notes in Artificial Intelligence, Vol. 5796. Springer, 813-824.
-
(2009)
Computational Collective Intelligence: Semantic Web, Social Networks and Multiagent Systems, N. Nguyen, R. Kowalczyk, and S. Chen, Eds. Lecture Notes in Artificial Intelligence
, vol.5796
, pp. 813-824
-
-
Lasota, T.1
Telec, Z.2
Trawinski, B.3
Trawinsky, K.4
-
71
-
-
0030342815
-
Combining estimates in regression and classification
-
Leblanc, M. and Tibshirani, R. 1996.Combining Estimates in Regression and Classification. J. Amer. Statist. Assoc. 91, 1641-1650.
-
(1996)
J. Amer. Statist. Assoc.
, vol.91
, pp. 1641-1650
-
-
Leblanc, M.1
Tibshirani, R.2
-
74
-
-
0033485370
-
Ensemble learning via negative correlation
-
Liu, Y. and Yao, X. 1999. Ensemble Learning Via Negative Correlation. Neural Netw. 12, 1399-1404.
-
(1999)
Neural Netw.
, vol.12
, pp. 1399-1404
-
-
Liu, Y.1
Yao, X.2
-
75
-
-
0034315099
-
Evolutionary ensembles with negative correlation learning
-
Liu, Y., Yao, X., and Higuchi, T. 2000. Evolutionary Ensembles With Negative Correlation Learning. Ieee Trans. Evolut.Comput. 4, 4, 380-387.
-
(2000)
IEEE Trans. Evolut.Comput.
, vol.4
, Issue.4
, pp. 380-387
-
-
Liu, Y.1
Yao, X.2
Higuchi, T.3
-
78
-
-
60349092310
-
An analysis of ensemble pruning techniques based on ordered aggregation
-
Martinez-Munoz, G., Hernandez-Lobato, D., and Suarez, A. 2009. An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation. Ieee Trans. Pattern Anal. Mach. Intell. 31, 245-259.
-
(2009)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.31
, pp. 245-259
-
-
Martinez-Munoz, G.1
Hernandez-Lobato, D.2
Suarez, A.3
-
80
-
-
33750460241
-
Using boosting to prune bagging ensembles
-
Martinez-Munoz, G. and Suarez, A. 2007. Using Boosting To Prune Bagging Ensembles. Pattern Recogn. Lett. 28, 1, 156-165.
-
(2007)
Pattern Recogn. Lett.
, vol.28
, Issue.1
, pp. 156-165
-
-
Martinez-Munoz, G.1
Suarez, A.2
-
83
-
-
84861377110
-
Comparing state-of-the-art regression methods for long term travel time prediction
-
Mendes-Moreira, J., Jorge, A. M., Freire De Sousa, J., and Soares, C. 2012.Comparing State-of-the-Art Regression Methods For Long Term Travel Time Prediction. Intell. Data Anal. 16, 3.
-
(2012)
Intell. Data Anal.
, Issue.16
, pp. 3
-
-
Mendes-Moreira, J.1
Jorge, A.M.2
Freire De Sousa, J.3
Soares, C.4
-
84
-
-
70350216169
-
Ensemble learning: A study on different variants of the dynamic selection approach
-
Springer
-
Mendes-Moreira, J., Jorge, A. M., Soares, C., and Freire De Sousa, J. 2009. Ensemble Learning: A Study on Different Variants of the Dynamic Selection Approach. in Proceedings of the 6th International Conference on Machine Learning and Data Mining, P. Perner, Ed. Lecture Notes in Computer Science, Vol. 5632. Springer, 191-205.
-
(2009)
Proceedings of the 6th International Conference on Machine Learning and Data Mining, P. Perner, Ed. Lecture Notes in Computer Science
, vol.5632
, pp. 191-205
-
-
Mendes-Moreira, J.1
Jorge, A.M.2
Soares, C.3
Freire De Sousa, J.4
-
85
-
-
77953694561
-
Skew estimation of document images using bagging
-
Meng, G., Pan, C., Zheng, N." and Sun, C. 2010. Skew Estimation of Document Images Using Bagging. Ieee Trans. Image Process. 19, 7, 1837-1846.
-
(2010)
IEEE Trans. Image Process.
, vol.19
, Issue.7
, pp. 1837-1846
-
-
Meng, G.1
Pan, C.2
Zheng, N.3
Sun, C.4
-
88
-
-
0032675169
-
A principal components approach to combining regression estimates
-
Merz, C. J. and Pazzani, M. J. 1999. A Principal Components Approach To Combining Regression Estimates. Mach. Learn. 36, 9-32.
-
(1999)
Mach. Learn.
, vol.36
, pp. 9-32
-
-
Merz, C.J.1
Pazzani, M.J.2
-
89
-
-
0242288813
-
The support vector machine under test
-
Meyer, D., Leisch, F., and Hornik, K. 2003. the Support Vector Machine Under Test. Neurocomput. 55, 1-2, 169-186.
-
(2003)
Neurocomput.
, vol.55
, Issue.1-2
, pp. 169-186
-
-
Meyer, D.1
Leisch, F.2
Hornik, K.3
-
90
-
-
84871222706
-
-
Mlg, U. D. 2011. http://Mlg.Ucd.Ie/
-
, vol.2011
-
-
Mlg, U.D.1
-
92
-
-
0038724494
-
Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data
-
Monti, S., Tamayo, P., Mesirov, J., and Golub, T. 2003. Consensus Clustering: A Resampling-Based Method For Class Discovery and Visualization of Gene Expression Microarray Data. Mach. Learn., 91- 118.
-
(2003)
Mach. Learn.
, pp. 91-118
-
-
Monti, S.1
Tamayo, P.2
Mesirov, J.3
Golub, T.4
-
93
-
-
84871189569
-
An ensemble regression approach for bus trip time prediction
-
Moreira, J. M., Sousa, J. F., Jorge, A. M., and Soares, C. 2006. An Ensemble Regression Approach For Bus Trip Time Prediction. in Proceedings of the Meeting of the Euro Working Group on Transportation. 317-321. http://www.Liaad.Up.Pt/Amjorge/Docs/Triana/Moreira06B.Pdf.
-
(2006)
Proceedings of the Meeting of the Euro Working Group on Transportation.
, pp. 317-321
-
-
Moreira, J.M.1
Sousa, J.F.2
Jorge, A.M.3
Soares, C.4
-
94
-
-
12844260916
-
Hybrid genetic algorithms for feature selection
-
Oh, I.-S., Lee, J.-S., and Moon, B.-R. 2004. Hybrid Genetic Algorithms For Feature Selection. Ieee Trans. Pattern Anal. Mach. Intell. 26, 11, 1424-1437.
-
(2004)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.26
, Issue.11
, pp. 1424-1437
-
-
Oh, I.-S.1
Lee, J.-S.2
Moon, B.-R.3
-
96
-
-
85156192015
-
Generating accurate and diverse members of a neural-network ensemble
-
Opitz, D. W. and Shavlik, J. W. 1996. Generating Accurate and Diverse Members of A Neural-Network Ensemble. Adv. Neural Inf. Process. Syst. 8, 535-541.
-
(1996)
Adv. Neural Inf. Process. Syst.
, vol.8
, pp. 535-541
-
-
Opitz, D.W.1
Shavlik, J.W.2
-
97
-
-
31144437540
-
Cixl2: A crossover operator for evolutionary algorithms based on population features
-
Ortiz-Boyer, D., Hervas-Martinez, C., and Garcia-Pendrajas, N. 2005. Cixl2: A Crossover Operator For Evolutionary Algorithms Based on Population Features. J. Artif. Intell. Res. 24, 1-48.
-
(2005)
J. Artif. Intell. Res.
, vol.24
, pp. 1-48
-
-
Ortiz-Boyer, D.1
Hervas-Martinez, C.2
Garcia-Pendrajas, N.3
-
98
-
-
0030352275
-
Reducing variance of committee prediction with resampling techniques
-
405-425
-
Parmanto, B., Munro, P, W., and Doyle, H. R. 1996. Reducing Variance of Committee Prediction With Resampling Techniques. Connect. Sci. 8, 3-4, 405-425.
-
(1996)
Connect. Sci.
, vol.8
, Issue.3-4
-
-
Parmanto, B.1
Munro P, W.2
Doyle, H.R.3
-
99
-
-
0030585190
-
Engineering multiversion neural-net systems
-
Partridge, D. and Yates, W. B. 1996. Engineering Multiversion Neural-Net Systems. Neural Comput. 8, 4, 869-893.
-
(1996)
Neural Comput.
, vol.8
, Issue.4
, pp. 869-893
-
-
Partridge, D.1
Yates, W.2
-
100
-
-
0000926506
-
When networks disagree: Ensemble methods for hybrid neural networks
-
R. Mammone, Ed. Chapman-Hall
-
Perrone, M. P. and Cooper, L. N. 1993. When Networks Disagree: Ensemble Methods For Hybrid Neural Networks. in Neural Networks For Speech and Image Processing, R. Mammone, Ed. Chapman-Hall.
-
(1993)
Neural Networks For Speech and Image Processing
-
-
Perrone, M.P.1
Cooper, L.N.2
-
101
-
-
77949901722
-
Ensemble learning
-
Polikar, R. 2009. Ensemble Learning. Scholarpedia 4, 1, 2776.
-
(2009)
Scholarpedia
, vol.4
, Issue.1
, pp. 2776
-
-
Polikar, R.1
-
102
-
-
85115260483
-
Floating search methods for feature selection with nonmonotonic criterion functions
-
Pudil, P., Ferri, F., Novovicova, J., and Kittler, J. 1994. Floating Search Methods For Feature Selection With Nonmonotonic Criterion Functions. in Proceedings of the Ieee International Conference on Pattern Recognition. Vol. 11. 279-283.
-
(1994)
Proceedings of the Ieee International Conference on Pattern Recognition.
, vol.11
, pp. 279-283
-
-
Pudil, P.1
Ferri, F.2
Novovicova, J.3
Kittler, J.4
-
103
-
-
84957702069
-
A dynamic integration algorithm for an ensemble of classifiers
-
Springer
-
Puuronen, S., Terziyan, V., and Tsymbal, A. 1999. A Dynamic Integration Algorithm For An Ensemble of Classifiers. in Proceedings of the International Symposium on Methodologies For Intelligent Systems. Lecture Notes in Computer Science, Vol. 1609. Springer, 592-600.
-
(1999)
Proceedings of the International Symposium on Methodologies For Intelligent Systems. Lecture Notes in Computer Science
, vol.1609
, pp. 592-600
-
-
Puuronen, S.1
Terziyan, V.2
Tsymbal, A.3
-
104
-
-
34548100943
-
Multi-classifier systems: Review and a roadmap for developers
-
Ranawana, R. and Palade, V. 2006. Multi-Classifier Systems: Review and A Roadmap For Developers. Int. J. Hybrid Intell. Syst. 3, 1, 35-61.
-
(2006)
Int. J. Hybrid Intell. Syst.
, vol.3
, Issue.1
, pp. 35-61
-
-
Ranawana, R.1
Palade, V.2
-
105
-
-
0036643047
-
Sparse regression ensembles in infinite and finite hypothesis spaces
-
Ratsch, G.,Demiriz, A., and Bennett, K. P. 2002. Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces. Mach. Learn. 48, 189-218.
-
(2002)
Mach. Learn.
, vol.48
, pp. 189-218
-
-
Ratsch G.Demiriz, A.1
Bennett, K.P.2
-
106
-
-
0030374103
-
Bootstrapping with noise: An effective regularization technique
-
Raviv, Y. and Intrator, N. 1996. Bootstrapping With Noise: An Effective Regularization Technique. Connect. Sci. 8, 3-4, 355-372.
-
(1996)
Connect. Sci.
, vol.8
, Issue.3-4
, pp. 355-372
-
-
Raviv, Y.1
Intrator, N.2
-
108
-
-
0141990695
-
Theoretical and empirical analysis of relieff and rrelieff
-
Robnik-Sikonja, M. and Kononenko, I. 2003. theoretical and Empirical Analysis of Relieff and Rrelieff. Mach. Learn. 53, 1-2, 23-69.
-
(2003)
Mach. Learn.
, vol.53
, Issue.1-2
, pp. 23-69
-
-
Robnik-Sikonja, M.1
Kononenko, I.2
-
109
-
-
33750095186
-
Rotation forest: A new classifier ensemble
-
Rodriguez, J. J., Kuncheva, L. I., and Alonso, C. J. 2006. Rotation Forest: A New Classifier Ensemble. Ieee Trans. Pattern Anal. Mach. Intell. 28, 10, 1619-1630.
-
(2006)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.28
, Issue.10
, pp. 1619-1630
-
-
Rodriguez, J.J.1
Kuncheva, L.I.2
Alonso, C.J.3
-
110
-
-
58549093526
-
Collective-agreement-based pruning of ensembles
-
Rokach, L. 2009A. Collective-Agreement-Based Pruning of Ensembles.Comput. Statist. Data Anal. 53, 1015-1026.
-
(2009)
Comput. Statist. Data Anal.
, vol.53
, pp. 1015-1026
-
-
Rokach, L.1
-
112
-
-
69449097857
-
Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography
-
Rokach, L. 2009C. Taxonomy For Characterizing Ensemble Methods in Classification Tasks: A Review and Annotated Bibliography.Comput. Statist. Data Anal. 53, 12, 4046-4072.
-
(2009)
Comput. Statist. Data Anal.
, vol.53
, Issue.12
, pp. 4046-4072
-
-
Rokach, L.1
-
113
-
-
75149176174
-
Ensemble-based classifiers
-
Rokach, L. 2010. Ensemble-Based Classifiers. Artif. Intell. Rev. 33, 1-39.
-
(2010)
Artif. Intell. Rev.
, Issue.33
, pp. 1-39
-
-
Rokach, L.1
-
114
-
-
84956994921
-
Methods for designing multiple classifier systems
-
Springer
-
Roli, F., Giacinto, G" and Vernazza, G. 2001. Methods For Designing Multiple Classifier Systems. in Proceedings of the International Workshop on Multiple Classifier Systems. Lecture Notes in Computer Science, Vol. 2096. Springer, 78-87.
-
(2001)
Proceedings of the International Workshop on Multiple Classifier Systems. Lecture Notes in Computer Science
, vol.2096
, pp. 78-87
-
-
Roli, F.1
Giacinto, G.2
Vernazza, G.3
-
115
-
-
33845445703
-
A weighted combination of stacking and dynamic integration
-
Rooney, N. and Patterson, D. 2007. A Weighted Combination of Stacking and Dynamic Integration. Pattern Recogn. 40, 4, 1385-1388.
-
(2007)
Pattern Recogn.
, vol.40
, Issue.4
, pp. 1385-1388
-
-
Rooney, N.1
Patterson, D.2
-
116
-
-
35048851039
-
Dynamic integration of regression models
-
Springer
-
Rooney, N., Patterson, D., Anand, S., and Tsymbal, A. 2004. Dynamic Integration of Regression Models. in Proceedings of the International Workshop on Multiple Classifier Systems. Lecture Notes in Computer Science, Vol. 3181. Springer, 164-173.
-
(2004)
Proceedings of the International Workshop on Multiple Classifier Systems. Lecture Notes in Computer Science
, vol.3181
, pp. 164-173
-
-
Rooney, N.1
Patterson, D.2
Anand, S.3
Tsymbal, A.4
-
117
-
-
0030367578
-
Ensemble learning using decorrelated neural networks
-
Rosen, B. E. 1996. Ensemble Learning Using Decorrelated Neural Networks. Connect. Sci. 8, 3-4, 373-383.
-
(1996)
Connect. Sci.
, vol.8
, Issue.3-4
, pp. 373-383
-
-
Rosen, B.E.1
-
119
-
-
0025448521
-
The strength of weak learnability
-
Schapire, R. 1990. the Strength of Weak Learnability. Mach. Learn. 5, 2, 197-227.
-
(1990)
Mach. Learn.
, vol.5
, Issue.2
, pp. 197-227
-
-
Schapire, R.1
-
121
-
-
72249112535
-
Ensemble methods for improving the performance of neighborhood-based collaborative filtering
-
Acm Press, New York
-
Schclar, A., Tsinkinovsky, A., Rokach, L., Meisels, A., and Antwarg, L. 2009. Ensemble Methods For Improving the Performance of Neighborhood-Based Collaborative Filtering. in Proceedings of the 3Rd Acm Conference on Recommender Systems (Recsys'09). Acm Press, New York, 261-264.
-
(2009)
Proceedings of the 3Rd Acm Conference on Recommender Systems (Recsys'09)
, pp. 261-264
-
-
Schclar, A.1
Tsinkinovsky, A.2
Rokach, L.3
Meisels, A.4
Antwarg, L.5
-
122
-
-
33745780111
-
Experiments with adaboost.rt, an improved boosting scheme for regression
-
Shrestha, D. L. and Solomatine, D. P. 2006. Experiments With Adaboost.Rt, An Improved Boosting Scheme For Regression. Neural Comput. 18, 1678-1710.
-
(2006)
Neural Comput.
, vol.18
, pp. 1678-1710
-
-
Shrestha, D.L.1
Solomatine, D.P.2
-
125
-
-
21844490598
-
Bounded-variable least squares: An algorithm and applications
-
Stark, P. and Parker, R. 1995. Bounded-Variable Least Squares: An Algorithm and Applications.Comput. Statist. 10, 2, 129-141.
-
(1995)
Comput. Statist.
, vol.10
, Issue.2
, pp. 129-141
-
-
Stark, P.1
Parker, R.2
-
126
-
-
0000629975
-
Cross-validatory choice and assessment of statistical predictions
-
Stone, M. 1974. Cross-Validatory Choice and Assessment of Statistical Predictions. J. Roy. Statist. Soc. B36, 2, 111-147.
-
(1974)
J. Roy. Statist. Soc.
, vol.B36
, Issue.2
, pp. 111-147
-
-
Stone, M.1
-
127
-
-
0041965980
-
Cluster ensembles: A knowledge reuse framework for combining multiple partitions
-
Strehl, A. and Ghosh, J. 2003. Cluster Ensembles: A Knowledge Reuse Framework For Combining Multiple Partitions. J. Mach. Learn. Res. 3, 583-617.
-
(2003)
J. Mach. Learn. Res.
, vol.3
, pp. 583-617
-
-
Strehl, A.1
Ghosh, J.2
-
130
-
-
77954062293
-
Predicting changes in protein thermostability brought about by single- or multi-site mutations
-
Tian, J.,Wu, N., Chu, X., and Fan, Y. 2010. Predicting Changes in Protein thermostability Brought About By Single- Or Multi-Site Mutations. Bmc Bioinf. 11, 370.
-
(2010)
Bmc Bioinf.
, Issue.11
, pp. 370
-
-
Tian J.Wu, N.1
Chu, X.2
Fan, Y.3
-
131
-
-
0037365188
-
Combining classifiers with meta decision trees
-
Todorovski, L. and Dzeroski, S. 2003.Combining Classifiers With Meta Decision Trees. Mach. Learn. 50, 3, 223-249.
-
(2003)
Mach. Learn.
, vol.50
, Issue.3
, pp. 223-249
-
-
Todorovski, L.1
Dzeroski, S.2
-
132
-
-
84871224675
-
-
Regression Datasets
-
Torgo, L. Regression Datasets. http://www.Liaad.Up.Pt/Ltorgo/Regression/ Datasets.html.
-
-
-
Torgo, L.1
-
133
-
-
85153970023
-
Combining estimators using non-constant weighting functions
-
Tresp, V. and Taniguchi, M. 1995.Combining Estimators Using Non-Constant Weighting Functions. Adv. Neural Inf. Process. Syst. 7, 419-426.
-
(1995)
Adv. Neural Inf. Process. Syst.
, vol.7
, pp. 419-426
-
-
Tresp, V.1
Taniguchi, M.2
-
134
-
-
33750293951
-
Diversified svm ensembles for large data sets
-
Springer
-
Tsang, I. W., Kocsor, A., and Kwok, J. T. 2006. Diversified Svm Ensembles For Large Data Sets. in Proceedings of the International Conference on Machine Learning. Lecture Notes in Artificial Intelligence, Vol. 4212. Springer, 792-800.
-
(2006)
Proceedings of the International Conference on Machine Learning. Lecture Notes in Artificial Intelligence
, vol.4212
, pp. 792-800
-
-
Tsang, I.W.1
Kocsor, A.2
Kwok, J.T.3
-
137
-
-
33750306177
-
Dynamic integration with random forests
-
Springer
-
Tsymbal, A., Pechenizkiy, M., and Cunningham, P. 2006A. Dynamic Integration With Random Forests. in Proceedings of the European Conference on Machine Learning (Ecml'06). Lecture Notes in Artificial Intelligence, Vol. 4212. Springer, 801-808.
-
(2006)
Proceedings of the European Conference on Machine Learning (Ecml'06). Lecture Notes in Artificial Intelligence
, vol.4212
, pp. 801-808
-
-
Tsymbal, A.1
Pechenizkiy, M.2
Cunningham, P.3
-
138
-
-
33750288387
-
-
Tech. Rep. Tcd-Cs-2006-2023, the University of Dublin, Trinity College
-
Tsymbal, A., Pechenizkiy, M., and Cunningham, P. 2006B. Dynamic Integration With Random Forests. Tech. Rep. Tcd-Cs-2006-23, the University of Dublin, Trinity College.
-
(2006)
Dynamic Integration With Random Forests
-
-
Tsymbal, A.1
Pechenizkiy, M.2
Cunningham, P.3
-
139
-
-
35348907876
-
Dynamic integration of classifiers for handling concept drift
-
Tsymbal, A., Pechenizkiy, M., Cunningham, P., and Puuronen, S. 2008. Dynamic Integration of Classifiers For Handling Concept Drift. Inf. Fusion 9, 1, 56-68.
-
(2008)
Inf. Fusion
, vol.9
, Issue.1
, pp. 56-68
-
-
Tsymbal, A.1
Pechenizkiy, M.2
Cunningham, P.3
Puuronen, S.4
-
142
-
-
0033117452
-
Soft combining of neural classifiers: A comparative study
-
Verikas, A., Lipnickas, A., Malmqvist, K., Becauskiene, M., and Gelzinis, A. 1999. Soft Combining of Neural Classifiers: A Comparative Study. Pattern Recogn. Lett. 20, 4, 429-444.
-
(1999)
Pattern Recogn. Lett.
, vol.20
, Issue.4
, pp. 429-444
-
-
Verikas, A.1
Lipnickas, A.2
Malmqvist, K.3
Becauskiene, M.4
Gelzinis, A.5
-
144
-
-
77952415079
-
Mining concept-drifting data streams using ensemble classifiers. In
-
Wang, H., Fan, W., Yu, P. S., and Han, J. 2003. Mining Concept-Drifting Data Streams Using Ensemble Classifiers. in Acm International Conference on Knowledge Discovery and Data Mining.
-
(2003)
Acm International Conference on Knowledge Discovery and Data Mining
-
-
Wang, H.1
Fan, W.2
Yu, P.S.3
Han, J.4
-
145
-
-
4344706336
-
Multistrategy ensemble learning: Reducing error by combining ensemble learning techniques
-
Webb, G. I. and Zheng, Z. 2004. Multistrategy Ensemble Learning: Reducing Error By Combining Ensemble Learning Techniques. Ieee Trans. Knowl. Data Engin. 16, 8, 980-991.
-
(2004)
IEEE Trans. Knowl. Data Engin.
, vol.16
, Issue.8
, pp. 980-991
-
-
Webb, G.I.1
Zheng, Z.2
-
146
-
-
77954760375
-
Comparing the effectiveness of several modeling methods for fault prediction
-
Weyuker, E. J., Ostrand, T. J., and Bell, R. M. 2010.Comparing the Effectiveness of Several Modeling Methods For Fault Prediction. Empir. Softw. Engin. 15, 277-295.
-
(2010)
Empir. Softw. Engin.
, vol.15
, pp. 277-295
-
-
Weyuker, E.J.1
Ostrand, T.J.2
Bell, R.M.3
-
149
-
-
84871183659
-
Data mining: Practical machine learning tools and techniques
-
Witten, I. H. and Frank, E. 2011. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
-
(2011)
Morgan Kaufmann
-
-
Witten, I.H.1
Frank, E.2
-
150
-
-
0026692226
-
Stacked generalization
-
Wolpert, D. H. 1992. Stacked Generalization. Neural Netw. 5, 2, 241-259.
-
(1992)
Neural Netw.
, vol.5
, Issue.2
, pp. 241-259
-
-
Wolpert, D.H.1
-
151
-
-
0031121318
-
Combination of multiple classifiers using local accuracy estimates
-
Woods, K. 1997.Combination of Multiple Classifiers Using Local Accuracy Estimates. Ieee Trans. Pattern Anal. Mach. Intell. 19, 4, 405-410.
-
(1997)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.19
, Issue.4
, pp. 405-410
-
-
Woods, K.1
-
152
-
-
0032028297
-
Feature subset selection using a genetic algorithm
-
Yang, J. and Honavar, V. 1997. Feature Subset Selection Using A Genetic Algorithm. Ieee Trans. Intell. Syst. 13, 2, 44-49.
-
(1997)
IEEE Trans. Intell. Syst.
, vol.13
, Issue.2
, pp. 44-49
-
-
Yang, J.1
Honavar, V.2
-
153
-
-
33750324642
-
Ensembles of nearest neighbor forecasts
-
Springer
-
Yankov, D., Decoste, D., and Keogh, E. 2006. Ensembles of Nearest Neighbor Forecasts. in Proceedings of the European Conference on Machine Learning. Lecture Notes in Artificial Intelligence, Vol. 4212. Springer, 545-556.
-
(2006)
Proceedings of the European Conference on Machine Learning. Lecture Notes in Artificial Intelligence
, vol.4212
, pp. 545-556
-
-
Yankov, D.1
Decoste, D.2
Keogh, E.3
-
154
-
-
0034844341
-
Neural network ensembles and their application to traffic flow prediction in telecommunications networks
-
Yao, X., Fischer, M., and Brown, G. 2001. Neural Network Ensembles and their Application To Traffic Flow Prediction in Telecommunications Networks. Neural Netw. 1, 693-698.
-
(2001)
Neural Netw.
, vol.1
, pp. 693-698
-
-
Yao, X.1
Fischer, M.2
Brown, G.3
-
156
-
-
0008538776
-
-
Mit Press, Chapter A Gradient-Based Boosting Algorithm For Regression Problems
-
Zemel, R. S. and Pitassi, T. 2001. Advances in Neuralinformation Processing Systems. Vol. 13. Mit Press, Chapter A Gradient-Based Boosting Algorithm For Regression Problems, 696-702.
-
(2001)
Advances in Neuralinformation Processing Systems.
, vol.13
, pp. 696-702
-
-
Zemel, R.S.1
Pitassi, T.2
-
158
-
-
37349035781
-
An empirical study of using rotation forest to improve regressors
-
Zhang, C.-X., Zhang, J.-S., and Wang, G.-W. 2008. An Empirical Study of Using Rotation Forest To Improve Regressors. Appl. Math.Comput. 195, 2, 618-629.
-
(2008)
Appl. Math.Comput.
, vol.195
, Issue.2
, pp. 618-629
-
-
Zhang, C.-X.1
Zhang, J.-S.2
Wang, G.-W.3
-
160
-
-
68749107987
-
A fast ensemble pruning algorithm based on pattern mining process
-
Zhao, Q.-L., Jiang, Y.-H., and Xu, M. 2009. A Fast Ensemble Pruning Algorithm Based on Pattern Mining Process. Data Min. Knowl. Discov. 19, 277-292.
-
(2009)
Data Min. Knowl. Discov.
, vol.19
, pp. 277-292
-
-
Zhao, Q.-L.1
Jiang, Y.-H.2
Xu, M.3
-
161
-
-
8344279588
-
Selective ensemble of decision trees
-
Springer
-
Zhou, Z.-H. and Tang, W. 2003. Selective Ensemble of Decision Trees. in Proceedings of the International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Lecture Notes in Artificial Intelligence, Vol. 2639. Springer, 476-483.
-
(2003)
Proceedings of the International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Lecture Notes in Artificial Intelligence
, vol.2639
, pp. 476-483
-
-
Zhou, Z.-H.1
Tang, W.2
-
162
-
-
0036567392
-
Ensembling neural networks: Many could be better than all
-
Zhou, Z.-H., Wu, J., and Tang, W. 2002. Ensembling Neural Networks: Many Could Be Better Than All. Artif. Intell. 137, 239-263.
-
(2002)
Artif. Intell.
, vol.137
, pp. 239-263
-
-
Zhou, Z.-H.1
Wu, J.2
Tang, W.3
|