Building ensemble classifiers using belief functions and OWA operators
暂无分享,去创建一个
[1] Ian Witten,et al. Data Mining , 2000 .
[2] Francesc Esteva,et al. Review of Triangular norms by E. P. Klement, R. Mesiar and E. Pap. Kluwer Academic Publishers , 2003 .
[3] Maria Petrou,et al. Use of Dempster-Shafer theory to combine classifiers which use different class boundaries , 2003, Pattern Analysis & Applications.
[4] David G. Stork,et al. Pattern Classification , 1973 .
[5] Thomas G. Dietterich. Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.
[6] Pierre Valin,et al. Data Fusion for Situation Monitoring, Incident Detection, Alert and Response Management , 2005 .
[7] L. Zadeh,et al. Data mining, rough sets and granular computing , 2002 .
[8] Lotfi A. Zadeh,et al. A COMPUTATIONAL APPROACH TO FUZZY QUANTIFIERS IN NATURAL LANGUAGES , 1983 .
[9] E. Mandler,et al. Combining the Classification Results of Independent Classifiers Based on the Dempster/Shafer Theory of Evidence , 1988 .
[10] Ronald R. Yager,et al. On ordered weighted averaging aggregation operators in multicriteria decisionmaking , 1988, IEEE Trans. Syst. Man Cybern..
[11] R. Suganya,et al. Data Mining Concepts and Techniques , 2010 .
[12] Ronald R. Yager,et al. An extension of the naive Bayesian classifier , 2006, Inf. Sci..
[13] Hakan Altinçay,et al. On the independence requirement in Dempster-Shafer theory for combining classifiers providing statistical evidence , 2006, Applied Intelligence.
[14] Marek Reformat. A fuzzy‐based multimodel system for reasoning about the number of software defects , 2005, Int. J. Intell. Syst..
[15] Thierry Denoeux,et al. Analysis of evidence-theoretic decision rules for pattern classification , 1997, Pattern Recognit..
[16] Karl Rihaczek,et al. 1. WHAT IS DATA MINING? , 2019, Data Mining for the Social Sciences.
[17] Mohamed A. Deriche,et al. A New Technique for Combining Multiple Classifiers using The Dempster-Shafer Theory of Evidence , 2002, J. Artif. Intell. Res..
[18] Galina L. Rogova,et al. Combining the results of several neural network classifiers , 1994, Neural Networks.
[19] Mübeccel Demirekler,et al. Speaker identification by combining multiple classifiers using Dempster-Shafer theory of evidence , 2003, Speech Commun..
[20] Michael J. Pazzani,et al. Error reduction through learning multiple descriptions , 2004, Machine Learning.
[21] Ronald R. Yager,et al. On ordered weighted averaging aggregation operators in multicriteria decision-making , 1988 .
[22] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[23] Alessandro Saffiotti,et al. The Transferable Belief Model , 1991, ECSQARU.
[24] Sargur N. Srihari,et al. Decision Combination in Multiple Classifier Systems , 1994, IEEE Trans. Pattern Anal. Mach. Intell..
[25] Thierry Denoeux. A k -Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory , 2008, Classic Works of the Dempster-Shafer Theory of Belief Functions.
[26] Thierry Denoeux,et al. A neural network classifier based on Dempster-Shafer theory , 2000, IEEE Trans. Syst. Man Cybern. Part A.
[27] Elisabetta Binaghi,et al. Fuzzy Dempster-Shafer reasoning for rule-based classifiers , 1999, Int. J. Intell. Syst..
[28] IVAN KRAMOSIL. Dempster Combination Rule with Boolean-Like Processed Belief Functions , 2001, Int. J. Uncertain. Fuzziness Knowl. Based Syst..
[29] Igor Kononenko,et al. Learning as Optimization: Stochastic Generation of Multiple Knowledge , 1992, ML.
[30] Smets Ph.,et al. Belief functions, Non-standard logics for automated reasoning , 1988 .
[31] Jiri Matas,et al. On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[32] Chris Carter,et al. Multiple decision trees , 2013, UAI.
[33] Wray L. Buntine,et al. A theory of learning classification rules , 1990 .
[34] Kamal A. Ali. A Comparison of Methods for Learning and Combining Evidence From Multiple Models , 1995 .
[35] R. Yager. Quantifier guided aggregation using OWA operators , 1996, Int. J. Intell. Syst..
[36] Saso Dzeroski,et al. Combining Multiple Models with Meta Decision Trees , 2000, PKDD.
[37] Peter E. Hart,et al. Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.
[38] Thierry Denoeux,et al. An evidence-theoretic k-NN rule with parameter optimization , 1998, IEEE Trans. Syst. Man Cybern. Part C.
[39] Subhash C. Bagui,et al. Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.
[40] R. Yager. Families of OWA operators , 1993 .
[41] Nikhil R. Pal,et al. Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory , 2006, IEEE Transactions on Geoscience and Remote Sensing.
[42] Hakan Altinçay,et al. A dempster-shafer theoretic framework for boosting based ensemble design , 2005, Pattern Analysis and Applications.
[43] Philippe Smets. Non-standard logics for automated reasoning , 1988 .
[44] Radko Mesiar,et al. Generated triangular norms , 2000, Kybernetika.
[45] Arthur P. Dempster,et al. Classic Works on the Dempster-Shafer Theory of Belief Functions (Studies in Fuzziness and Soft Computing) , 2007 .
[46] B. J. Winer. Statistical Principles in Experimental Design , 1992 .
[47] Ronald R. Yager,et al. Generalized Naive Bayesian Modeling , 2008 .
[48] Glenn Shafer,et al. A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.
[49] Ludmila I. Kuncheva,et al. Combining Pattern Classifiers: Methods and Algorithms , 2004 .
[50] Ronald R. Yager,et al. Extending multicriteria decision making by mixing t‐norms and OWA operators , 2005, Int. J. Intell. Syst..
[51] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.
[52] Witold Pedrycz,et al. Data Mining Methods for Knowledge Discovery , 1998, IEEE Trans. Neural Networks.
[53] M. O’Hagan. A Fuzzy Neuron Based on Maximum Entropy Ordered Weighted Averaging , 1990, 1990 Conference Record Twenty-Fourth Asilomar Conference on Signals, Systems and Computers, 1990..