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Exploiting remote learners in Internet environment with agents

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Abstract

Data in the Internet are scattered on different sites indeliberately, and accumulated and updated frequently but not synchronously. It is infeasible to collect all the data together to train a global learner for prediction; even exchanging learners trained on different sites is costly. In this paper, aggregative-learning is proposed. In this paradigm, every site maintains a local learner trained from its own data. Upon receiving a request for prediction, an aggregative-learner at a local site activates and sends out many mobile agents taking the request to potential remote learners. The prediction of the aggregative-learner is made by combining the local prediction and the responses brought back by the agents. Theoretical analysis and simulation experiments show the superiority of the proposed method.

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References

  1. Lazarevic A, Obradovic Z. The distributed boosting algorithm. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 2001. 311–316

  2. Tsoumakas G, Vlahavas I. Effective stacking of distributed classifiers. In: Proceedings of the 15th European Conference on Artificial Intelligence, Lyon, France, 2002. 340–344

  3. Caragea C, Caragea D, Honavar V. Learning support vector machines from distributed data sources. In: Proceedings of the 20th National Conference on Artificial Intelligence, Pittsburgh, PA, 2005. 1602–1603

  4. Aoun-Allah M, Mineau G. Distributed data mining: Why do more than aggregating models. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, 2007. 2645–2650

  5. Bowyer K, Chawla N, Moore T, et al. A parallel decision tree builder for mining very large visualization datasets. In: Proceedings of 13th IEEE International Conference on Systems, Man, and Cybernetics, Nashville, TN, 2000. 1888–1893

  6. Caragea D, Silvescu A, Honavar V. Decision tree induction from distributed heterogeneous autonomous data sources. In: Proceedings of the 3rd International Conference on Intelligent Systems Design and Applications, Tulsa, 2003. 341–350

  7. Breiman L. Pasting bites together for prediction in large data sets. Mach Learn, 1999, 36: 85–103

    Article  Google Scholar 

  8. Chawla N V, Hall L O, Bowyer K W, et al. Learning ensembles from bites: A scalable and accurate approach. J Mach Learn Research, 2004, 5: 421–451

    MathSciNet  Google Scholar 

  9. Luo P, Xiong H, Lu K, et al. Distributed classification in peer-to-peer networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, 2007. 968–976

  10. Zhong N, Liu J, Yao Y. In search of the wisdom web. IEEE Comput, 2002, 35: 27–31

    Google Scholar 

  11. Zhou Z H. Ensemble. In: Liu L, Özsu T, eds. Encyclopedia of Database Systems, Berlin: Springer, 2009

    Google Scholar 

  12. Esposito R, Saitta L. Monte Carlo theory as an explanation of bagging and boosting. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence, Acapulco, Mexico, 2003. 499–504

  13. Freund Y, Mansour Y, Schapire R E. Why averaging classifiers can protect against overfitting. In: Proceedings of the 8th International Workshop on Artificial Intelligence and Statistics, Key West, FL, 2001

  14. Friedman J, Hastie T, Tibshirani R. Additive logistic regression: A statistical view of Boosting with discussions. Ann Stat, 2000, 28: 337–407

    Article  MATH  MathSciNet  Google Scholar 

  15. Blake C, Keogh E, Merz C J. UCI repository of machine learning databases. [http://www.ics.uci.edu/mlearn/MLRepository.html], Department of Information and Computer Science, University of California, Irvine, 1998

    Google Scholar 

  16. Witten I H, Frank E. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2005

  17. Zhou Z H, Wu J, Tang W. Ensembling neural networks: Many could be better than all. Artif Intell, 2002. 137: 239–263

    Article  MATH  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, China

    Ming Li, Wei Wang & ZhiHua Zhou

Authors
  1. Ming Li
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  2. Wei Wang
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  3. ZhiHua Zhou
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Correspondence to ZhiHua Zhou.

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Li, M., Wang, W. & Zhou, Z. Exploiting remote learners in Internet environment with agents. Sci. China Ser. F-Inf. Sci. 53, 64–76 (2010). https://doi.org/10.1007/s11432-010-0011-2

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  • DOI: https://doi.org/10.1007/s11432-010-0011-2

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