Многоагентная технология принятия решений в задачах объединения данных
Аннотация
В статье рассматривается технология поддержки принятия решений на основе информации, полученной из различных источников. Специфика технологии разработки таких систем обусловлена распределенностью источников и гетерогенностью представленных в них данных, распределенным характером функционирования таких систем поддержки принятия решений, а также распределенным характером процесса разработки ее базовых компонент. Эти особенности ведут к необходимости решения ряда специфических задач технологического характера. Модели, методы и архитектурные решения, касающиеся технологии разработки систем поддержки принятия решений на основе распределенных гетерогенных источников данных, а также их реализация в форме программной инструментальной системы составляют основное содержание данной работы.Литература
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Ting K.and Witten I. Issues in stacked generalization // Journal of Artificial Intelligence Research, 10, 1999. — p. 271–289.
Todorovski L.and Dzeroski S. Combining classifiers with meta decision trees // D.A.Zighen, J.Komarowski and J.Zitkov (Eds.) Proceedings of 4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD–2000), Springer Verlag, 2000. — p. 54–64.
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D.Wolpert Stacked generalization // Neural Network, 5(2), 1992. — p. 241–260.
Городецкий В., Самойлов В. Визуальный синтез классифицирующих предикатов и их применение для мета-классификации // Труды Таганрогского радиотехнического института, 4, 2001, — с. 5–16.
Растригин Л., Эренштейн Р. Методы коллективного распознавания. Москва, Энергоиздат, 1981, — 102 с.
Ali K. and Pazzani M. Error reduction through learning multiple descriptions // Machine Learning, 24(3), 1996. — с. 173–202
Bass T. Intrusion Detection System and Multisensor Data Fusion: Creating Cyberspace Situational Awareness // Communication of the ACM, vol. 43, no. 4, 2000. — p. 99–105.
Bay S. and Pazzani M. Characterizing model error and differences // Proceedings of 17th International Conference on machine learning (ICML–2000), Morgan Kaufmann, 2000.
Breiman L. Bagging predictors // Machine Learning, 24 (2), 1996. — p. 123–140.
Breiman L. Stacked regression // Machine Learning, 24(1), 1996, — p. 49–64.
Buntine W.L. A theory of learning classification rules // Ph.D thesis, University of Technology, School of Computing Science, Sydney, Australia, 1990.
Chan P.and Stolfo S. A comparative evaluation of voting and meta-learning on partitioned data // Proceedings of Twelfth 4th International Conference on machine Learning, Tahoe City, CA,1995, — p. 90–98.
Clark P.and Niblett P. The CN2 induction algorithm // Machine learning, 3(4), 1989, — p. 261– 283.
Clemen R. Combining forecasts: A review and annotated bibliography // International Journal of Forecasting, 5, 1989. — p. 559–583.
Corkill D and Lesser V. The use of meta-level control for coordination in distributed problem solving network // Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI–83), Menlo Park, CA, 1983. —p. 767–770.
Cost S. and Salzberg S. A weighted nearest neighbor algorithm for learning with symbolic features // Machine Learning, 10(1), 1993 — p. 57–78.
Dietterich T. Machine Learning Research: Four Current Directions // AI magazine. 18(4), 1997. —p. 97–136.
Dietterich T. Ensemble Methods in Machine Learning // M.Arbib (Ed.) Handbook of Brain Theory and Neural Networks, 2nd Edition, MIT Press, 2001.
Freund Y.and Shapire R. Experiments with a new boosting algorithm // L.Saitta (Ed.) Machine Learning. Proceedings of the 13th International Conference. Morgan Kaufmann, 1996.
Gama J.and Brazdil P. Cascade generalization // Machine Learning, 41(3), 2000. — p. 315–342.
Goodman I., Mahler R., and Nguen H. Mathematics of Data Fusion // Kluwer Academic Publishers, 1997.
Gorodetski V. Bayes' Inference and Decision Making in Artificial Intelligence Systems" // Industrial Applications of Artificial Intelligence. North-Holland, 1991. — p. 276–281.
Gorodetski V.and Karsayev O. Algorithm of Rule Extraction from Learning Data // Proceedings of the 8th International Conference "Expert Systems & Artificial Intelligence" (EXPERSYS– 96),1996. — p. 133–138.
Gorodetski V., Skormin V., Popyack L., and Karsaev O. Distributed Learning in a Data Fusion System // Proceedings of the Conference of the World Computer Congress (WCC–2000) "Intelligent Information Processing" (IIP2000), Beijing, 2000. — p. 147–154.
Hashem S. Optimal linear combination of neural networks // Ph.D. thesis, Purdue University, School of Industrial Engineering, Lafaette, IN, 1997.
Jordan M.and Jacobs R. Hierarchical mixtures of experts and the EM algorithm // Neural Computations, 6(2), 1994. — p. 181–214.
"KAON – The KArlsruhe Ontology and Semantic Web Infrastructure". http://kaon.semanticweb.org/
http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
Mason C. and Johnson K. DATMS: A framework for distributed assumption-based reasoning // Distributed Artificial Intelligence, vol. 2, Eds.M.Hurbis and L.Gasser. CA, Morgan Kaufmann, 1989. — p. 293–318.
Merz C. Combining classifiers using correspondence analysis // Advances in Neural Information Processing, 1997.
Merz C.and Murphy P. UCI repository of machine learning databases. Irvine, CA, University of California, Department of Information and Computer Science, 1997 http://www.ics.uci.edu/!mlearn/MLR repository.html.
Michalski R. A Theory and Methodology of Inductive Learning // Machine Learning, vol.1, Eds. J.G.Carbonel, R.S.Michalski, and T.M.Mitchel, Tigoda, Palo Alto, 1983. — p. 83–134.
Michalski R.and Kaufmann K. The AQ19 System for Machine Learning and Pattern discovery: A General Description and User's guide. George Mason University. Technical Report ML01–2, P01–2, 2001.
Murthy S., Kassif S., Salzberg S., and Beigel R. OC1: Randomized Induction of oblique decision trees // Proceedings of AAAI–93, AAAI Press, 1993.
Niyogi P., Pierrot J–B., and Siohan O. "Multiple Classifiers by constrained minimization" // Proceedings of International Conference on Acoustics, Speech, and Signal Processing, Istanbul, Turkey, June, 2000.
Ortega J., Coppel M., Argamon S. Arbitraining Among Competing Classifiers Using Learned Referees // Knowledge and Information Systems 3 (2001) 4, 2001. — p. 470–490.
Perrone M. and Cooper L. When networks disagree: Ensemble methods for hybrid neural networks // R.J.Mammone (Ed.), Neural Networks for Speech and Image Processing, Chapman and Hall, 1993.
Prodomidis A., Chan P., and Stolfo S. Meta–learning in distributed data mining systems: Issues and approaches // P.Chan and H.Kargupta (Eds.) Advances in Distributed Data Mining, AAAI Press, 1999. Also available at http://www.cs.columbia.edu/~sal/hpapers/DDMBOOK.ps.gz.
Quinlan R. C4.5 Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, 1993.
Rumelhart D., Hinton G., and Williams R. Learning internal representation by error propagation // D.Rumelhart, J.McClelland (Eds.) Parallel Distributed Processing: Exploration of the microstructure of cognitions, Volume 1: Foundations. MIT Press, 1986.
Seewald A.and Fuernkranz J. An evaluation of grading classifiers // Proceedings of 4th International Conference "Intelligent data Analysis", LNCS 2189, 2001. — p. 115–124.
Sementic Web roadmap. http://www.w3.org/DesignIssues/Semantic.html
Ting K. The characterization of predictive accuracy and decision combination // Proceedings of 13th International Conference on Machine Learning, Morgan Kaufman, 1996. — p. 498–506.
Ting K.and Witten I. Issues in stacked generalization // Journal of Artificial Intelligence Research, 10, 1999. — p. 271–289.
Todorovski L.and Dzeroski S. Combining classifiers with meta decision trees // D.A.Zighen, J.Komarowski and J.Zitkov (Eds.) Proceedings of 4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD–2000), Springer Verlag, 2000. — p. 54–64.
Todorovski L.and Dzeroski S. Combining multiple models classifiers with meta decision trees // To appear in Machine Learning Journal. 2001.
Vilalta R.and Drissi Y. A perspective view and survey of meta-learning // Submitted to the Journal of Artificial Intelligence Review, http://www.research.ibm.com/people/v/vilalta/papers/jaireview01.ps
D.Wolpert Stacked generalization // Neural Network, 5(2), 1992. — p. 241–260.
Multi-agent data fusion technology
Abstract
The paper is devoted to the problems associated with the development of data fusion systems. Peculiarities of any data fusion development technology are caused several reasons. The most distributed mode of data fusion system operation and also distributed character of the development technology itself. These peculiarities entail a need to solve a number of very specific technologyoriented tasks. Models, methods and architectural decisions associated with the above development technology and also technology support mean implemented as a specialized software tool are the focus of the paper.Опубликован
2002-04-01
Как цитировать
Городецкий, Карсаев, & Самойлов,. (2002). Многоагентная технология принятия решений в задачах объединения данных. Труды СПИИРАН, 2(1), 12-37. https://doi.org/10.15622/sp.1.1
Раздел
Статьи
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