Ensemble-based classifiers

Rokach, Lior

doi:10.1007/s10462-009-9124-7

Cited by 2,657Machine Learning and Data ClassificationImbalanced Data Classification TechniquesForecasting Techniques and Applications

Ensemble-based classifiers

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Rokach, Lior·2010·Artificial Intelligence Review

DOI 10.1007/s10462-009-9124-7

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Abstract

The idea of ensemble methodology is to build a predictive model by integrating multiple models. It is well-known that ensemble methods can be used for improving prediction performance. Researchers from various disciplines such as statistics and AI considered the use of ensemble methodology. This paper, review existing ensemble techniques and can be served as a tutorial for practitioners who are interested in building ensemble based systems.

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The improvement in classification accuracy when using PCs from four tasks together is consistent with the underlying theorem of ensemble-based classification.
“This is consistent with the underlying theorem of ensemble-based classification (Rokach, 2010; Klöppel et al., 2012).”
From Functional neuroimaging of visuospatial working memory tasks enables accurate detection of attention deficit and hyperactivity disorder (2015) · cites this paper as Rokach, 2010
Classifying participants using several measurements (e.g., fMRI data from few distinct VSWM tasks), where each measurement enables better than chance classification and there is substantial independency between measurements, enabled higher classification accuracies compared to using data from each single measurement.
“As expected based on this theorem, classifying participants by using several measurements for each participant (e.g., fMRI data from few distinct VSWM tasks), where each measurement enables a better than chance classification accuracy, and where there is a substantial degree of independency between the measurements (e.g., distinct cognitive tasks), enabled higher classification accuracies compared”
From Functional neuroimaging of visuospatial working memory tasks enables accurate detection of attention deficit and hyperactivity disorder (2015) · cites this paper as Rokach, 2010

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Random Forests
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Measuring the efficiency of decision making units
Charnes, A., Cooper, W. W., & Rhodes, E. (1978) · European Journal of Operational Research · doi:10.1016/0377-2217(78)90138-8 · Cited by 29,767 · see citing works
Untitled work
(1992) · Cited by 23,431 · see citing works
Bagging predictors
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325 · see citing works
The Analytic Hierarchy Process
Saaty, T. L., & Kearns, K. P. (1985) · Elsevier eBooks · doi:10.1016/b978-0-08-032599-6.50008-8 · Cited by 15,407 · see citing works
Exploratory Data Analysis
David, F. N., & Tukey, J. W. (1977) · Biometrics · doi:10.2307/2529486 · Cited by 12,707 · see citing works
Experiments with a new boosting algorithm
Freund, Y., & Schapire, R. E. (1996) · Cited by 8,189 · see citing works
Stacked generalization
Wolpert, D. H. (1992) · Neural Networks · doi:10.1016/s0893-6080(05)80023-1 · Cited by 7,951 · see citing works
Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors)
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884 · see citing works
The random subspace method for constructing decision forests
Ho, T. K. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.709601 · Cited by 6,874 · see citing works
A comparison of methods for multiclass support vector machines
Hsu, C., & Lin, C. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/72.991427 · Cited by 6,861 · see citing works
A detailed analysis of the KDD CUP 99 data set
Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009) · 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications · doi:10.1109/cisda.2009.5356528 · Cited by 5,396 · see citing works
The Strength of Weak Learnability
Schapire, R. E. (1990) · Machine Learning · doi:10.1007/bf00116037 · Cited by 4,600 · see citing works
Neural network ensembles
Hansen, L. K., & Salamon, P. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.58871 · Cited by 4,260 · see citing works
A scaled conjugate gradient algorithm for fast supervised learning
Møller, M. F. (1993) · Neural Networks · doi:10.1016/s0893-6080(05)80056-5 · Cited by 4,094 · see citing works
Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
Dietterich, T. G. (1998) · Neural Computation · doi:10.1162/089976698300017197 · Cited by 3,840 · see citing works
Combining Pattern Classifiers
Kuncheva, L. I. (2004) · Wiley eBooks · doi:10.1002/0471660264 · Cited by 3,007 · see citing works
Advances in knowledge discovery and data mining
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90007-3 · Cited by 2,996 · see citing works
Ensemble based systems in decision making
Robi Polikar, A. (2006) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2006.1688199 · Cited by 2,960 · see citing works
Hierarchical Mixtures of Experts and the EM Algorithm
Jordan, M. I., & Jacobs, R. A. (1994) · Neural Computation · doi:10.1162/neco.1994.6.2.181 · Cited by 2,730 · see citing works
Solving Multiclass Learning Problems via Error-Correcting Output Codes
Dietterich, T. G., & Bakiri, G. (1995) · Journal of Artificial Intelligence Research · doi:10.1613/jair.105 · Cited by 2,698 · see citing works
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
Bauer, E. J., & Kohavi, R. (1999) · Machine Learning · doi:10.1023/a:1007515423169 · Cited by 2,627 · see citing works
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy
Kuncheva, L. I., & Whitaker, C. J. (2003) · Machine Learning · doi:10.1023/a:1022859003006 · Cited by 2,335 · see citing works
30 years of adaptive neural networks: perceptron, Madaline, and backpropagation
Widrow, B. C., & Lehr, M. A. (1990) · Proceedings of the IEEE · doi:10.1109/5.58323 · Cited by 2,241 · see citing works
Ensembling neural networks: Many could be better than all
Zhou, Z.-H., Wu, J., & Tang, W. (2002) · Artificial Intelligence · doi:10.1016/s0004-3702(02)00190-x · Cited by 1,964 · see citing works
Neural Network Ensembles, Cross Validation, and Active Learning
Krogh, A., & Vedelsby, J. (1994) · Cited by 1,838 · see citing works
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Keerthi, S. S., Shevade, S., Bhattacharyya, C., & Murthy, K. R. K. (2001) · Neural Computation · doi:10.1162/089976601300014493 · Cited by 1,791 · see citing works
Knowledge Discovery in Databases
Piateski, G., & Frawley, W. (1991) · Medical Entomology and Zoology · Cited by 1,746 · see citing works
Boosting a Weak Learning Algorithm by Majority
Freund, Y. (1995) · Information and Computation · doi:10.1006/inco.1995.1136 · Cited by 1,674 · see citing works
Cost-sensitive boosting for classification of imbalanced data
Sun, Y., Kamel, M. S., Wong, A. K. C., & Wang, Y. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.04.009 · Cited by 1,429 · see citing works
Scaling up the accuracy of Naive-Bayes classifiers: a decision-tree hybrid
Kohavi, R. (1996) · Cited by 1,404 · see citing works
Classifier Fitness Based on Accuracy
Wilson, S. W. (1995) · Evolutionary Computation · doi:10.1162/evco.1995.3.2.149 · Cited by 1,395 · see citing works
Data Mining with Decision Trees - Theory and Applications
Rokach, L., & Maimon, O. (2007) · Series in machine perception and artificial intelligence · doi:10.1142/9789812771728 · Cited by 1,284 · see citing works
Self-citation
Bagging, boosting, and C4.S
Quinlan, J. R. (1996) · National Conference on Artificial Intelligence · Cited by 1,251 · see citing works
Arcing classifier (with discussion and a rejoinder by the author)
Breiman, L. (1998) · The Annals of Statistics · doi:10.1214/aos/1024691079 · Cited by 1,104 · see citing works
Diversity creation methods: a survey and categorisation
Gavin Brown, B., Wyatt, J., Harris, R., & Yao, X. (2004) · Information Fusion · doi:10.1016/j.inffus.2004.04.004 · Cited by 1,003 · see citing works
Combination of multiple classifiers using local accuracy estimates
Woods, K. S., Kegelmeyer, W. P., & Bowyer, K. W. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.588027 · Cited by 957 · see citing works
Ensemble selection from libraries of models
Caruana, R., Niculescu-Mizil, A., Crew, G., & Ksikes, A. (2004) · International Conference on Machine Learning · doi:10.1145/1015330.1015432 · Cited by 894 · see citing works
Is Combining Classifiers with Stacking Better than Selecting the Best One?
Džeroski, S., & Ženko, B. (2004) · Machine Learning · doi:10.1023/b:mach.0000015881.36452.6e · Cited by 890 · see citing works
Rule induction with CN2: Some recent improvements
Clark, P. A., & Boswell, R. (1991) · Lecture notes in computer science · doi:10.1007/bfb0017011 · Cited by 871 · see citing works
Top-Down Induction of Decision Trees Classifiers—A Survey
Rokach, L., & Maimon, O. (2005) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2004.843247 · Cited by 799 · see citing works
Self-citation
Support vector machines for multi-class pattern recognition.
Weston, J., & Watkins, C. (1999) · Cited by 784 · see citing works
Knowledge-based artificial neural networks
Towell, G. G., & SHAVLIK, J. W. (1994) · Artificial Intelligence · doi:10.1016/0004-3702(94)90105-8 · Cited by 751 · see citing works
Complexity measures of supervised classification problems
Ho, T. K., & Basu, M. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.990132 · Cited by 740 · see citing works
Some Studies in Machine Learning Using the Game of Checkers. II—Recent Progress
Samuel, A. L. (1967) · IBM Journal of Research and Development · doi:10.1147/rd.116.0601 · Cited by 700 · see citing works
Bayesian Networks for Data Mining
Heckerman, D. E. (1997) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009730122752 · Cited by 631 · see citing works
Error Correlation and Error Reduction in Ensemble Classifiers
Tumer, K., & Ghosh, J. (1996) · Connection Science · doi:10.1080/095400996116839 · Cited by 595 · see citing works
Advances in Instance Selection for Instance-Based Learning Algorithms
Brighton, H., & Mellish, C. (2002) · Data Mining and Knowledge Discovery · doi:10.1023/a:1014043630878 · Cited by 534 · see citing works
Bagging, Boosting and the Random Subspace Method for Linear Classifiers
Skurichina, M., & Duin, R. P. W. (2002) · Pattern Analysis and Applications · doi:10.1007/s100440200011 · Cited by 522 · see citing works
The Need for Biases in Learning Generalizations
Mitchell, T. M. (2007) · Cited by 520 · see citing works
On Combining Artificial Neural Nets
SHARKEY, A. J. C. (1996) · Connection Science · doi:10.1080/095400996116785 · Cited by 516 · see citing works
Pruning Adaptive Boosting
Margineantu, D. D., & Dietterich, T. G. (1997) · Cited by 514 · see citing works
Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets
Bryll, R., Gutiérrez‐Osuna, R., & Quek, F. K. H. (2003) · Pattern Recognition · doi:10.1016/s0031-3203(02)00121-8 · Cited by 481 · see citing works
Selecting a classification method by cross-validation
Schaffer, C. (1993) · Machine Learning · doi:10.1023/a:1022639714137 · Cited by 476 · see citing works
A Comparison of Decision Tree Ensemble Creation Techniques
Banfield, R. E., Hall, L., Bowyer, K. W., & Kegelmeyer, W. P. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.250609 · Cited by 453 · see citing works
Accuracy-Based Learning Classifier Systems: Models, Analysis and Applications to Classification Tasks
Bernadó-Mansilla, E., & Garrell-Guiu, J. M. (2003) · Evolutionary Computation · doi:10.1162/106365603322365289 · Cited by 358 · see citing works
A composite classifier system design: Concepts and methodology
Dasarathy, B. V., & Sheela, B. V. (1979) · Proceedings of the IEEE · doi:10.1109/proc.1979.11321 · Cited by 344 · see citing works
Pasting Small Votes for Classification in Large Databases and On-Line
Breiman, L. (1999) · Machine Learning · doi:10.1023/a:1007563306331 · Cited by 318 · see citing works
Rule-Based Expert Systems
Castillo, E., Gutiérrez, J. M., & Hadi, A. S. (1997) · Texts and monographs in computer science · doi:10.1007/978-1-4612-2270-5_2 · Cited by 313 · see citing works
Feature selection for ensembles
Opitz, D. W. (1999) · Cited by 312 · see citing works
A constructive algorithm for training cooperative neural network ensembles
Islam, M. M., Yao, X., & Murase, K. (2003) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2003.813832 · Cited by 293 · see citing works
Generating Accurate and Diverse Members of a Neural-Network Ensemble
Opitz, D. W., & SHAVLIK, J. W. (1995) · Cited by 279 · see citing works
A Survey of Methods for Scaling Up Inductive Algorithms
Provost, F. J., & Kolluri, V. (1999) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009876119989 · Cited by 274 · see citing works
Back Propagation is Sensitive to Initial Conditions
Kolen, J. F., & Pollack, J. B. (1990) · Cited by 271 · see citing works
Ensemble Pruning Via Semi-definite Programming
Zhang, Y., Burer, S., & Street, W. N. (2006) · doi:10.5555/1248547.1248595 · Cited by 265 · see citing works
Constructing diverse classifier ensembles using artificial training examples
Melville, P., & Mooney, R. J. (2003) · Cited by 263 · see citing works
NONLINEAR GATED EXPERTS FOR TIME SERIES: DISCOVERING REGIMES AND AVOIDING OVERFITTING
Weigend, A. S., Mangeas, M., & Srivastava, A. N. (1995) · International Journal of Neural Systems · doi:10.1142/s0129065795000251 · Cited by 256 · see citing works
Ensemble Learning Using Decorrelated Neural Networks
Rosen, B. E. (1996) · Connection Science · doi:10.1080/095400996116820 · Cited by 247 · see citing works
Advances in Distributed and Parallel Knowledge Discovery
Kargupta, H., & Chan, P. K. (2000) · Medical Entomology and Zoology · Cited by 234 · see citing works
Engineering Multiversion Neural-Net Systems
Partridge, D. G., & Yates, W. B. (1996) · Neural Computation · doi:10.1162/neco.1996.8.4.869 · Cited by 218 · see citing works
Using Correspondence Analysis to Combine Classifiers
Merz, C. J. (1999) · Machine Learning · doi:10.1023/a:1007559205422 · Cited by 209 · see citing works
Nearest neighbor classification from multiple feature subsets
Bay, S. D. (1999) · Intelligent Data Analysis · doi:10.1016/s1088-467x(99)00018-9 · Cited by 186 · see citing works
Diversity in multiple classifier systems
Kuncheva, L. I. (2004) · Information Fusion · doi:10.1016/j.inffus.2004.04.009 · Cited by 167 · see citing works
Ensemble feature selection with the simple Bayesian classification
Tsymbal, A., Puuronen, S. J., & Patterson, D. W. (2003) · Information Fusion · doi:10.1016/s1566-2535(03)00004-6 · Cited by 161 · see citing works
On the Accuracy of Meta-learning for Scalable Data Mining
Chan, P. K., & Stolfo, S. J. (1997) · Journal of Intelligent Information Systems · doi:10.1023/a:1008640732416 · Cited by 158 · see citing works
Toward parallel and distributed learning by meta-learning
Chan, P. K., & Stolfo, S. J. (1993) · Cited by 151 · see citing works
A Comparative Evaluation of Voting and Meta-learning on Partitioned Data
Chan, P. K., & Stolfo, S. J. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50020-7 · Cited by 149 · see citing works
Bayesian Inference in Mixtures-of-Experts and Hierarchical Mixtures-of-Experts Models with an Application to Speech Recognition
Peng, F., Jacobs, R. A., & Tanner, M. A. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476965 · Cited by 149 · see citing works
New Results on Error Correcting Output Codes of Kernel Machines
Passerini, A., Pontil, M., & Frasconi, P. (2004) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2003.820841 · Cited by 149 · see citing works
Recursive Automatic Bias Selection for Classifier Construction
Brodley, C. E. (1995) · Machine Learning · doi:10.1007/bf00993475 · Cited by 125 · see citing works
Learning Ensembles from Bites: A Scalable and Accurate Approach
Chawla, N. V., Hall, L., Bowyer, K. W., et al. (2004) · doi:10.5555/1005332.1005347 · Cited by 122 · see citing works
Selective Ensemble of Decision Trees
Zhou, Z., & Tang, W. (2007) · Springer eBooks · doi:10.1007/3-540-39205-x_81 · Cited by 121 · see citing works
Methods of Combining Multiple Classifiers with Different Features and Their Applications to Text-Independent Speaker Identification
Chen, K., Wang, L., & Chi, H. (1997) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001497000196 · Cited by 121 · see citing works
Genetic algorithm-based feature set partitioning for classification problems
Rokach, L. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.10.013 · Cited by 114 · see citing works
Self-citation
Neural and statistical classifiers-taxonomy and two case studies
Holmström, L., Koistinen, P., Laaksonen, J. T., & Erkki Oja, A. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.554187 · Cited by 112 · see citing works
Decomposition in data mining: an industrial case study
Kusiak, A. (2000) · IEEE Transactions on Electronics Packaging Manufacturing · doi:10.1109/6104.895081 · Cited by 112 · see citing works
A simplified neural network solution through problem decomposition: the case of the truck backer-upper
Jenkins, R. E., & Yuhas, B. P. (1993) · IEEE Transactions on Neural Networks · doi:10.1109/72.238326 · Cited by 105 · see citing works
The Meta-Pi network: building distributed knowledge representations for robust multisource pattern recognition
Hampshire, J. B., & Waibel, A. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.142911 · Cited by 103 · see citing works
EROS: Ensemble rough subspaces
Hu, Q., Yu, D., Xie, Z., & Li, X. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.04.022 · Cited by 95 · see citing works
Evaluation of Adaptive Mixtures of Competing Experts
Nowlan, S. J., & Hinton, G. E. (1990) · neural information processing systems · Cited by 94 · see citing works
On the Boosting Pruning Problem
Tamon, C., & Xiang, J. (2000) · Lecture notes in computer science · doi:10.1007/3-540-45164-1_41 · Cited by 93 · see citing works
Nonlinear Boosting Projections for Ensemble Construction
García‐Pedrajas, N., García‐Osorio, C., & Fyfe, C. (2007) · Cited by 89 · see citing works
The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins
Rudin, C. D., Daubechies, I. C., & Schapire, R. E. (2004) · doi:10.5555/1005332.1044712 · Cited by 88 · see citing works
Combining Decision Trees and Neural Networks for Drug Discovery
Langdon, W. B., Barrett, S. J., & Buxton, B. F. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45984-7_6 · Cited by 85 · see citing works
Using rough sets theory and database operations to construct a good ensemble of classifiers for data mining applications
Hu, X. T. (2002) · Proceedings · doi:10.1109/icdm.2001.989524 · Cited by 79 · see citing works
Input decimated ensembles
Tumer, K., & Oza, N. C. (2003) · Pattern Analysis and Applications · doi:10.1007/s10044-002-0181-7 · Cited by 78 · see citing works
A NARMAX model representation for adaptive control based on local models
Johansen, T. A., & Foss, B. A. (1992) · Modeling Identification and Control A Norwegian Research Bulletin · doi:10.4173/mic.1992.1.3 · Cited by 77 · see citing works
Cost Complexity-Based Pruning of Ensemble Classifiers
Prodromidis, A. L., & Stolfo, S. J. (2001) · Knowledge and Information Systems · doi:10.1007/pl00011678 · Cited by 71 · see citing works
Decision-tree instance-space decomposition with grouped gain-ratio
Cohen, S., Rokach, L., & Maimon, O. (2007) · Information Sciences · doi:10.1016/j.ins.2007.01.016 · Cited by 68 · see citing works
Self-citation
Graphical models for discovering knowledge
Buntine, W. (1996) · Cited by 65 · see citing works
Multiple binary decision tree classifiers
Shlien, S. (1990) · Pattern Recognition · doi:10.1016/0031-3203(90)90098-6 · Cited by 65 · see citing works
Decomposition Methodology for Knowledge Discovery and Data Mining
Maimon, O., & Rokach, L. (2005) · Series in machine perception and artificial intelligence · doi:10.1142/5686 · Cited by 65 · see citing works
Self-citation
A local boosting algorithm for solving classification problems
Zhang, C., & Zhang, J. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.06.015 · Cited by 56 · see citing works
Decomposition methodology for classification tasks: a meta decomposer framework
Rokach, L. (2006) · Pattern Analysis and Applications · doi:10.1007/s10044-006-0041-y · Cited by 55 · see citing works
Self-citation
Decomposition Methodology for Knowledge Discovery and Data Mining: Theory and Applications
Maimon, O., & Rokach, L. (2005) · Cited by 54 · see citing works
Self-citation
Feature set decomposition for decision trees
Rokach, L., & Maimon, O. (2005) · Intelligent Data Analysis · doi:10.3233/ida-2005-9202 · Cited by 49 · see citing works
Self-citation
Diversity between Neural Networks and Decision Trees for Building Multiple Classifier Systems
Wang, W., Jones, P., & Partridge, D. (2000) · Lecture notes in computer science · doi:10.1007/3-540-45014-9_23 · Cited by 48 · see citing works
Parallelizing AdaBoost by weights dynamics
Merler, S., Caprile, B., & Furlanello, C. (2006) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2006.09.001 · Cited by 48 · see citing works
Collective-agreement-based pruning of ensembles
Rokach, L. (2008) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2008.12.001 · Cited by 47 · see citing works
Self-citation
Nearest neighbor classification from multiple feature subsets
Bay, S. D. (1999) · Intelligent Data Analysis · doi:10.3233/ida-1999-3304 · Cited by 47 · see citing works
Structurally adaptive modular networks for nonstationary environments
Ramamurti, V., & Ghosh, J. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.737501 · Cited by 46 · see citing works
Improving Supervised Learning by Feature Decomposition
Maimon, O., & Rokach, L. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45758-5_12 · Cited by 44 · see citing works
Self-citation
Feature transformation by function decomposition
Zupan, B., Bohanec, M., Demšar, J., et al. (1998) · IEEE Intelligent Systems and their Applications · doi:10.1109/5254.671090 · Cited by 41 · see citing works
Space Decomposition in Data Mining: A Clustering Approach
Rokach, L., Maimon, O., & Lavi, I. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-39592-8_5 · Cited by 40 · see citing works
Self-citation
A new hybrid approach in combining multiple experts to recognise handwritten numerals
Rahman, F., & Fairhurst, M. C. (1997) · Pattern Recognition Letters · doi:10.1016/s0167-8655(97)00078-0 · Cited by 38 · see citing works
A Linear-Bayes Classifier
da Gama, J. M. P. (2000) · Lecture notes in computer science · doi:10.1007/3-540-44399-1_28 · Cited by 34 · see citing works
Classifier evaluation under limited resources
Arbel, R., & Rokach, L. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2006.03.008 · Cited by 34 · see citing works
Self-citation
Problem decomposition and the learning of skills
Michie, D. (1995) · Lecture notes in computer science · doi:10.1007/3-540-59286-5_46 · Cited by 33 · see citing works
SELECTIVE VOTING — GETTING MORE FOR LESS IN SENSOR FUSION
Rokach, L., Maimon, O., & Arbel, R. (2006) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001406004739 · Cited by 33 · see citing works
Self-citation
Use of methodological diversity to improve neural network generalisation
Yates, W. B., & Partridge, D. (1996) · Neural Computing and Applications · doi:10.1007/bf01413747 · Cited by 28 · see citing works
Modular Neural Networks for Medical Prognosis: Quantifying the Benefits of Combining Neural Networks for Survival Prediction
Ohno‐Machado, L., & Musen, M. A. (1997) · Connection Science · doi:10.1080/095400997116748 · Cited by 27 · see citing works
Variance Optimized Bagging
Derbeko, P., El‐Yaniv, R., & Meir, R. (2002) · Lecture notes in computer science · doi:10.1007/3-540-36755-1_6 · Cited by 25 · see citing works
Signature Table Systems and Learning
Biermann, A. W., Fairfield, J. R. C., & Beres, T. R. (1982) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1982.4308882 · Cited by 25 · see citing works
Ensemble feature selection with the simple Bayesian classification in medical diagnostics
Tsymbal, A., & Puuronen, S. J. (2003) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2002.1011381 · Cited by 24 · see citing works
Negative Correlation Learning and the Ambiguity Family of Ensemble Methods
Brown, G., & Wyatt, J. (2003) · Lecture notes in computer science · doi:10.1007/3-540-44938-8_27 · Cited by 23 · see citing works
Approximate Splitting for Ensembles of Trees using Histograms
Kamath, C., Cantú‐Paz, E., & Littau, D. (2002) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972726.22 · Cited by 20 · see citing works
Robust Order Statistics Based Ensembles for Distributed Data Mining
Tumer, K., & Ghosh, J. (2001) · doi:10.21236/ada396346 · Cited by 20 · see citing works
An Empirical Comparison of Pruning Methods for Ensemble Classifiers
Windeatt, T., & Ardeshir, G. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44816-0_21 · Cited by 20 · see citing works
IMPROVING SUPERVISED LEARNING BY SAMPLE DECOMPOSITION
Rokach, L., Maimon, O., & Arad, O. (2005) · International Journal of Computational Intelligence and Applications · doi:10.1142/s146902680500143x · Cited by 20 · see citing works
Self-citation
Reject Strategies Driven Combination of Pattern Classifiers
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An Empirical Study of Building Compact Ensembles
Liu, H., Mandvikar, A., & Mody, J. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27772-9_63 · Cited by 17 · see citing works
Decomposition Methodology For Knowledge Discovery And Data Mining: Theory And Applications (Machine Perception and Artificial Intelligence)
Maimon, O. (2005) · Cited by 16 · see citing works
Comparative evaluation of algorithms for effective data leakage detection
Kumar, A., Goyal, A., Kumar, A., Chaudhary, N. K., & Kamath, S. S. (2013) · IEEE Conference on Information and Communication Technologies (ICT) · doi:10.1109/cict.2013.6558085 · Cited by 13 · see citing works
Generate Different Neural Networks by Negative Correlation Learning
Liu, Y. (2005) · Lecture notes in computer science · doi:10.1007/11539087_17 · Cited by 11 · see citing works
Fast Learning Algorithm for Controlling Logistic Chaotic System Based on Chebyshev Neural Network
Li, M., He, Y., Tan, W., et al. (2009) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2009.258 · Cited by 2 · see citing works

Flagged first, then by citations

Research works printed but not linked: 13

E. Summary, "Data Leakage Worldwide: Common Risks and Mistakes Employees Make," Europe, pp. 1-8, 2008.
W. L. W. Lee, S. J. Stolfo, and K. W. Mok, "A data mining framework for building intrusion detection models," IEEE Symp. Secur. Priv., vol. 00, no. c, pp. 120-132, 1999. DOI:10.1109/SECPRI.1999.766909 · DOI
C. Guevara, M. Santos and J. A. Martín, "Método para la Detección de Intrusos basado en la Sinergia de Técni- cas de Inteligencia Artificial," in Proceedings of the IV Congreso Español de Informática CEDI 2013, pp. 963- 972.
NSL-KDD. Disponible en: http://nsl.cs.unb.ca/NSL- KDD/
J. McHugh, "Testing Intrusion detection systems: a critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lin- coln Laboratory," ACM Transactions on Informa- tion and System Security, vol. 3. pp. 262-294, 2000. DOI:10.1145/382912.382923 · DOI
J. R. Quinlan, "Induction of decision trees," Mach. Learn., vol. 1, no. 1, pp. 81-106, 1986. DOI:10.1023/A:1022643204877 · DOI · Paper page
J. C. Platt, "Sequential minimal optimization: A fast algorithm for training support vector machines," Adv. Kernel Methods Support Vector Learn., vol. 208, pp. 1-21, 1998.
P. Domingos and M. Pazzani, "On the Optimality of the Simple Bayesian Classifier under Zero-One Los," Mach. Learn., vol. 29, no. 2-3, pp. 103-130, 1997.
D. Pyle, Data Preparation for Data Mining, 1st ed., vol. 1. San Francisco: Morgan Kaufmann, 1999. DOI:10.1023/A:1007413511361 · DOI
F. Ceballos, L. E. Muñoz, and J. Moreno, "Selección de perceptrones multicapa usando aprendizaje bayesiano," Sci. Tech., no. 49, pp. 110-115, 2011.
L. Rokach, "Ensemble-based classifiers," Artif. Intell. Rev., vol. 33, no. 1-2, pp. 1-39, 2010. DOI:10.1007/ s10462-009-9124-7
H. Liu and R. Setiono, "Feature Selection and Classifica- tion: A Probabilistic Wrapper Approach," in Proceedings of the 9th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, 1996, pp. 419-424.
J. Alcala-Fdez, L. Sanchez, S. Garcia, M. J. del Jesus, S. Ventura, J. M. Garrell, J. Otero, C. Romero, J. Bacardit, V. M. Rivas, J. C. Fernandez, and F. Herrera, "KEEL: a software tool to assess evolutionary algorithms for data mining problems," Soft Comput., vol. 13, no. 3, pp. 307- 318, 2009. Disponible: //www.keel.es/

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Authors: Rokach, L.
Affiliations: Ben-Gurion University of the Negev (IL)
Corresponding author: Rokach, Lior
ORCID: Rokach, Lior
Venue: Artificial Intelligence Review
Publisher: Springer Science and Business Media LLC
Published: 2009-11-19
Volume: 33
Issue: 1-2
Pages: 1-39
ISSN: 0269-2821 (ISSN-L)
1573-7462
DOI: 10.1007/s10462-009-9124-7
OpenAlex: W2155806188
Cited by: 2,657
References: 135
Citations by year: 2026: 111 · 2025: 162 · 2024: 193 · 2023: 214 · 2022: 228 · 2021: 242 · 2020: 261 · 2019: 266 · 2018: 205 · 2017: 172 · 2016: 155 · 2015: 146 · 2014: 100 · 2013: 86 · 2012: 59
FWCI: 41.87
Type: journal article
Language: en
Open access: diamond
revistascientificas.cuc.edu.co
Licence: cc-by-nc-nd
www.springer.com/tdm (tdm)
Locations: Artificial Intelligence Review (publishedVersion)
Inge CUC (publishedVersion, cc-by-nc-nd)
DSPACE System - Metalibrary (University of the Coast) (acceptedVersion, other-oa)
Dialnet (Universidad de la Rioja) (submittedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
Keywords: ensemble methods, ensemble classifiers, model integration, predictive performance, classifier combination

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