Ensemble-based classifiers
暂无分享,去创建一个
[1] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[2] Chandrika Kamath,et al. Approximate Splitting for Ensembles of Trees using Histograms , 2001, SDM.
[3] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.
[4] A. U.S.,et al. Measuring the efficiency of decision making units , 2003 .
[5] Lior Rokach,et al. Collective-agreement-based pruning of ensembles , 2009, Comput. Stat. Data Anal..
[6] Yoav Freund,et al. Boosting a weak learning algorithm by majority , 1995, COLT '90.
[7] Erkki Oja,et al. Neural and statistical classifiers-taxonomy and two case studies , 1997, IEEE Trans. Neural Networks.
[8] Lior Rokach,et al. Improving Supervised Learning by Feature Decomposition , 2002, FoIKS.
[9] Foster J. Provost,et al. A Survey of Methods for Scaling Up Inductive Algorithms , 1999, Data Mining and Knowledge Discovery.
[10] S. Sohn,et al. Ensemble Based on Data Envelopment Analysis , 2001 .
[11] Robbie T. Nakatsu,et al. Rule‐Based Expert Systems , 2009 .
[12] Yong Liu,et al. Generate Different Neural Networks by Negative Correlation Learning , 2005, ICNC.
[13] William B. Yates,et al. Use of methodological diversity to improve neural network generalisation , 2005, Neural Computing & Applications.
[14] C. Brodley. Recursive Automatic Bias Selection for Classifier Construction , 2004, Machine Learning.
[15] Peter Clark,et al. Rule Induction with CN2: Some Recent Improvements , 1991, EWSL.
[16] Salvatore J. Stolfo,et al. Cost Complexity-Based Pruning of Ensemble Classifiers , 2001, Knowledge and Information Systems.
[17] Cynthia Rudin,et al. The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins , 2004, J. Mach. Learn. Res..
[18] William B. Langdon,et al. Combining Decision Trees and Neural Networks for Drug Discovery , 2002, EuroGP.
[19] Kevin W. Bowyer,et al. Combination of multiple classifiers using local accuracy estimates , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[20] Ran El-Yaniv,et al. Variance Optimized Bagging , 2002, ECML.
[21] Robert P. W. Duin,et al. Bagging, Boosting and the Random Subspace Method for Linear Classifiers , 2002, Pattern Analysis & Applications.
[22] Jude W. Shavlik,et al. Knowledge-Based Artificial Neural Networks , 1994, Artif. Intell..
[23] Ivan Bratko,et al. Feature Transformation by Function Decomposition , 1998, IEEE Intell. Syst..
[24] Chih-Jen Lin,et al. A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.
[25] Geoffrey E. Hinton,et al. Evaluation of Adaptive Mixtures of Competing Experts , 1990, NIPS.
[26] Alexey Tsymbal,et al. Ensemble feature selection with the simple Bayesian classification in medical diagnostics , 2002, Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002).
[27] Alexey Tsymbal,et al. Ensemble feature selection with the simple Bayesian classification , 2003, Inf. Fusion.
[28] Rich Caruana,et al. Ensemble selection from libraries of models , 2004, ICML.
[29] L. Breiman. Arcing classifier (with discussion and a rejoinder by the author) , 1998 .
[30] J. Ross Quinlan,et al. Bagging, Boosting, and C4.5 , 1996, AAAI/IAAI, Vol. 1.
[31] Nitesh V. Chawla,et al. Learning Ensembles from Bites: A Scalable and Accurate Approach , 2004, J. Mach. Learn. Res..
[32] Yang Wang,et al. Cost-sensitive boosting for classification of imbalanced data , 2007, Pattern Recognit..
[33] Wray L. Buntine,et al. Graphical models for discovering knowledge , 1996, KDD 1996.
[34] Nicolás García-Pedrajas,et al. Nonlinear Boosting Projections for Ensemble Construction , 2007, J. Mach. Learn. Res..
[35] Cesare Furlanello,et al. Parallelizing AdaBoost by weights dynamics , 2007, Comput. Stat. Data Anal..
[36] Robert A. Jacobs,et al. Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.
[37] John F. Kolen,et al. Backpropagation is Sensitive to Initial Conditions , 1990, Complex Syst..
[38] Wei Tang,et al. Ensembling neural networks: Many could be better than all , 2002, Artif. Intell..
[39] Lior Rokach,et al. Decomposition methodology for classification tasks: a meta decomposer framework , 2006, Pattern Analysis and Applications.
[40] Lior Rokach,et al. Genetic algorithm-based feature set partitioning for classification problems , 2008, Pattern Recognit..
[41] Raymond J. Mooney,et al. Constructing Diverse Classifier Ensembles using Artificial Training Examples , 2003, IJCAI.
[42] Seymour Shlien,et al. Multiple binary decision tree classifiers , 1990, Pattern Recognit..
[43] Fuad Rahman,et al. A new hybrid approach in combining multiple experts to recognise handwritten numerals , 1997, Pattern Recognit. Lett..
[44] Joydeep Ghosh,et al. Structurally adaptive modular networks for nonstationary environments , 1999, IEEE Trans. Neural Networks.
[45] Christopher J. Merz,et al. Using Correspondence Analysis to Combine Classifiers , 1999, Machine Learning.
[46] Lior Rokach,et al. Decomposition Methodology for Knowledge Discovery and Data Mining - Theory and Applications , 2005, Series in Machine Perception and Artificial Intelligence.
[47] Kagan Tumer,et al. Robust Order Statistics Based Ensembles for Distributed Data Mining , 2001 .
[48] Oleksandr Makeyev,et al. Neural network with ensembles , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).
[49] Lior Rokach,et al. Decision-tree instance-space decomposition with grouped gain-ratio , 2007, Inf. Sci..
[50] Ludmila I. Kuncheva,et al. Combining Pattern Classifiers: Methods and Algorithms , 2004 .
[51] Cullen Schaffer,et al. Selecting a classification method by cross-validation , 1993, Machine Learning.
[52] R. Schapire. The Strength of Weak Learnability , 1990, Machine Learning.
[53] Huan Liu,et al. An Empirical Study of Building Compact Ensembles , 2004, WAIM.
[54] Ludmila I. Kuncheva. Diversity in multiple classifier systems , 2005, Inf. Fusion.
[55] Jason Weston,et al. Support vector machines for multi-class pattern recognition , 1999, ESANN.
[56] Salvatore J. Stolfo,et al. Toward parallel and distributed learning by meta-learning , 1993 .
[57] Lawrence O. Hall,et al. A Comparison of Decision Tree Ensemble Creation Techniques , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[58] Mark A. Musen,et al. Modular Neural Networks for Medical Prognosis: Quantifying the Benefits of Combining Neural Networks for Survival Prediction , 1997, Connect. Sci..
[59] Eric Bauer,et al. An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.
[60] Alan W. Biermann,et al. Signature Table Systems and Learning , 1982, IEEE Transactions on Systems, Man, and Cybernetics.
[61] Laurent Mascarilla,et al. Reject Strategies Driven Combination of Pattern Classifiers , 2002, Pattern Analysis & Applications.
[62] William B. Yates,et al. Engineering Multiversion Neural-Net Systems , 1996, Neural Computation.
[63] Kagan Tumer,et al. Error Correlation and Error Reduction in Ensemble Classifiers , 1996, Connect. Sci..
[64] M. Field,et al. Robust Order Statistics based Ensembles for Distributed Data Mining , 2000 .
[65] Donald Michie,et al. Problem Decomposition and the Learning of Skills , 1995, ECML.
[66] Lior Rokach,et al. Selective Voting - Getting More for Less in Sensor Fusion , 2006, Int. J. Pattern Recognit. Artif. Intell..
[67] Ashok N. Srivastava,et al. Nonlinear gated experts for time series: discovering regimes and avoiding overfitting , 1995, Int. J. Neural Syst..
[68] Cullen Schaffer,et al. Technical Note: Selecting a Classification Method by Cross-Validation , 1993, Machine Learning.
[69] William Nick Street,et al. Ensemble Pruning Via Semi-definite Programming , 2006, J. Mach. Learn. Res..
[70] Bernard Zenko,et al. Is Combining Classifiers with Stacking Better than Selecting the Best One? , 2004, Machine Learning.
[71] Gavin Brown,et al. Negative Correlation Learning and the Ambiguity Family of Ensemble Methods , 2003, Multiple Classifier Systems.
[72] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[73] Wei Tang,et al. Selective Ensemble of Decision Trees , 2003, RSFDGrC.
[74] Qinghua Hu,et al. EROS: Ensemble rough subspaces , 2007, Pattern Recognit..
[75] Thomas G. Dietterich,et al. Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..
[76] Xin Yao,et al. A constructive algorithm for training cooperative neural network ensembles , 2003, IEEE Trans. Neural Networks.
[77] Andrew Kusiak,et al. Decomposition in data mining: an industrial case study , 2000 .
[78] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[79] F. Provost. A Survey of Methods for Scaling Up Inductive Learning Algorithms , 1997 .
[80] Tin Kam Ho,et al. The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[81] Stephen D. Bay. Nearest neighbor classification from multiple feature subsets , 1999, Intell. Data Anal..
[82] Ron Kohavi,et al. Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid , 1996, KDD.
[83] Leo Breiman,et al. Pasting Small Votes for Classification in Large Databases and On-Line , 1999, Machine Learning.
[84] T. Saaty,et al. The Analytic Hierarchy Process , 1985 .
[85] Ke Chen,et al. Methods of Combining Multiple Classifiers with Different Features and Their Applications to Text-Independent Speaker Identification , 1997, Int. J. Pattern Recognit. Artif. Intell..
[86] Ludmila I. Kuncheva,et al. Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy , 2003, Machine Learning.
[87] Amanda J. C. Sharkey,et al. On Combining Artificial Neural Nets , 1996, Connect. Sci..
[88] Lior Rokach,et al. Classifier evaluation under limited resources , 2006, Pattern Recognit. Lett..
[89] João Gama. A Linear-Bayes Classifier , 2000, IBERAMIA-SBIA.
[90] Paolo Frasconi,et al. New results on error correcting output codes of kernel machines , 2004, IEEE Transactions on Neural Networks.
[91] T. Johansen,et al. A NARMAX model representation for adaptive control based on local models , 1992 .
[92] Wray L. Buntine,et al. A theory of learning classification rules , 1990 .
[93] Tom M. Mitchell,et al. The Need for Biases in Learning Generalizations , 2007 .
[94] Xin Yao,et al. Diversity creation methods: a survey and categorisation , 2004, Inf. Fusion.
[95] Lior Rokach,et al. Data Mining with Decision Trees - Theory and Applications , 2007, Series in Machine Perception and Artificial Intelligence.
[96] Anders Krogh,et al. Neural Network Ensembles, Cross Validation, and Active Learning , 1994, NIPS.
[97] Salvatore J. Stolfo,et al. A Comparative Evaluation of Voting and Meta-learning on Partitioned Data , 1995, ICML.
[98] Thomas G. Dietterich,et al. Pruning Adaptive Boosting , 1997, ICML.
[99] A. L. Samuel,et al. Some studies in machine learning using the game of checkers. II: recent progress , 1967 .
[100] William Frawley,et al. Knowledge Discovery in Databases , 1991 .
[101] B.V. Dasarathy,et al. A composite classifier system design: Concepts and methodology , 1979, Proceedings of the IEEE.
[102] KohaviRon,et al. An Empirical Comparison of Voting Classification Algorithms , 1999 .
[103] Chun-Xia Zhang,et al. A local boosting algorithm for solving classification problems , 2008, Comput. Stat. Data Anal..
[104] R. Polikar,et al. Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.
[105] Christian Lang,et al. Bi-decomposition of function sets using multi-valued logic , 2003, Ausgezeichnete Informatikdissertationen.
[106] Francis K. H. Quek,et al. Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets , 2003, Pattern Recognit..
[107] Kagan Tumer,et al. Input decimated ensembles , 2003, Pattern Analysis & Applications.
[108] Kurt Hornik,et al. A Cluster Ensembles Framework , 2003, HIS.
[109] Paul W. Munro,et al. Improving Committee Diagnosis with Resampling Techniques , 1995, NIPS.
[110] Lior Rokach,et al. Top-down induction of decision trees classifiers - a survey , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[111] J. Friedman. Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .
[112] Derek Partridge,et al. Diversity between Neural Networks and Decision Trees for Building Multiple Classifier Systems , 2000, Multiple Classifier Systems.
[113] Lior Rokach,et al. Space Decomposition in Data Mining: A Clustering Approach , 2002, ISMIS.
[114] David H. Wolpert,et al. Stacked generalization , 1992, Neural Networks.
[115] Lior Rokach,et al. Feature set decomposition for decision trees , 2005, Intell. Data Anal..
[116] Bruce E. Rosen,et al. Ensemble Learning Using Decorrelated Neural Networks , 1996, Connect. Sci..
[117] David W. Opitz,et al. Generating Accurate and Diverse Members of a Neural-Network Ensemble , 1995, NIPS.
[118] Salvatore J. Stolfo,et al. On the Accuracy of Meta-learning for Scalable Data Mining , 2004, Journal of Intelligent Information Systems.
[119] Xiaohua Hu,et al. Using rough sets theory and database operations to construct a good ensemble of classifiers for data mining applications , 2001, Proceedings 2001 IEEE International Conference on Data Mining.
[120] Robert E. Jenkins,et al. A simplified neural network solution through problem decomposition: the case of the truck backer-upper , 1993, IEEE Trans. Neural Networks.
[121] Alexander H. Waibel,et al. The Meta-Pi Network: Building Distributed Knowledge Representations for Robust Multisource Pattern Recognition , 1992, IEEE Trans. Pattern Anal. Mach. Intell..
[122] David W. Opitz,et al. Feature Selection for Ensembles , 1999, AAAI/IAAI.
[123] Fengchun Peng,et al. Bayesian Inference in Mixtures-of-Experts and Hierarchical Mixtures-of-Experts Models With an Applic , 1996 .
[124] Wen Tan,et al. Fast Learning Algorithm for Controlling Logistic Chaotic System Based on Chebyshev Neural Network , 2009, 2009 Fifth International Conference on Natural Computation.
[125] Terry Windeatt,et al. An Empirical Comparison of Pruning Methods for Ensemble Classifiers , 2001, IDA.
[126] Lior Rokach,et al. Improving Supervised Learning by Sample Decomposition , 2005, Int. J. Comput. Intell. Appl..
[127] John W. Tukey,et al. Exploratory Data Analysis. , 1979 .
[128] Christino Tamon,et al. On the Boosting Pruning Problem , 2000, ECML.