Table of Contents
Advances in Artificial Intelligence
Volume 2012, Article ID 790485, 8 pages
Research Article

A Stochastic Hyperheuristic for Unsupervised Matching of Partial Information

Distributed Computing Systems, Belfast, UK

Received 28 May 2012; Accepted 21 September 2012

Academic Editor: Thomas Mandl

Copyright © 2012 Kieran Greer. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Licas,
  2. K. Greer, “A stochastic hyper-heuristic for optimising through comparisons,” in Proceedings of the 3rd International Symposium on Knowledge Acquisition and Modeling (KAM'10), pp. 325–328, IEEE, Wuhan, China, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. K. Greer, “Literature review for the multi-source intelligence project called “a stochastic hyper-heuristic for optimising through comparisons”,” Distributed Computing Systems Research Report, 2011, View at Google Scholar
  4. E. K. Burke, M. Hyde, G. Kendall, G. Ochoa, E. Ozcan, and R. Qu, “A survey of hyper-heuristics,” Computer Science Technical Report NOTTCS-TR-SUB-0906241418-2747, University of Nottingham, 2009, (hhSurvey09). View at Google Scholar
  5. M. Bader-El-Den and R. Poli, “Generating SAT Local-Search Heuristics using a GP Hyper-Heuristic Framework,” in Proceedings of the 8th International Conference on Artificial Evolution (EA'07), pp. 37–49, 2007.
  6. E. Ozcan, M. Misir, and E. K. Burke, “A self-organising hyper-heuristic framework,” in Proceedings of the 4th Multidisciplinary International Scheduling Conference: Theory & Applications (MISTA'09), pp. 784–787, Dublin, Ireland, August 2009.
  7. J. G. Marín-Blázquez and S. Schulenburg, “Multi-step environment learning classifier systems applied to hyper-heuristics,” in Proceedings of the 8th Annual Genetic and Evolutionary Computation Conference, pp. 1521–1528, Washington, DC, USA, July 2006. View at Scopus
  8. R. Bai, J. Blazewicz, E. K. Burke, G. Kendall, and B. McCollum, “A simulated annealing hyper-heuristic methodology for flexible decision support,” Tech. Rep. NOTTCS-TR-2007-8, School of CSiT, University of Nottingham, 2007. View at Google Scholar
  9. T. P. Runarsson and X. Yao, “Stochastic ranking for constrained evolutionary optimization,” IEEE Transactions on Evolutionary Computation, vol. 4, no. 3, pp. 284–294, 2000. View at Publisher · View at Google Scholar · View at Scopus
  10. N. Sahoo, J. Callan, R. Krishnan, G. Duncan, and R. Padman, “Incremental hierarchical clustering of text documents,” in Proceedings of the 15th ACM Conference on Information and Knowledge Management (CIKM'06), pp. 357–366, New York, NY, USA, November 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” Journal of Machine Learning Research, vol. 3, pp. 1157–1182, 1993. View at Google Scholar
  12. A. L. Blum and P. Langley, “Selection of relevant features and examples in machine learning,” Artificial Intelligence, vol. 97, no. 1-2, pp. 245–271, 1997. View at Google Scholar · View at Scopus
  13. R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artificial Intelligence, vol. 97, no. 1-2, pp. 273–324, 1997. View at Google Scholar · View at Scopus
  14. S. McClean, B. Scotney, K. Greer, and R. Pairceir, “Conceptual clustering of heterogeneous distributed databases,” in Proceedings of the 12th Joint European Conference on Machine Learning (ECML'01) and 5th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'01), Workshop on Ubiquitous Data Mining for Mobile and Distributed Environments, pp. 46–55, September 2001.
  15. J. J. Rocchio, “Relevance feedback in information retrieval,” in The SMART Retrieval System: Experiments in Automatic Document Processing, G. Salton, Ed., pp. 313–323, Prentice Hall, Englewood Cliffs, NJ, USA, 1971. View at Google Scholar
  16. G. Guo, H. Wang, D. Bell, Y. Bi, and K. Greer, “An kNN model-based approach and its application in text categorization,” in Computational Linguistics and Intelligent Text Processing, 5th International Conference, Cicling 2004, Seoul, Korea, A. Gelbukh, Ed., pp. 559–570, Springer, New York, NY, USA, 2004. View at Google Scholar
  17. D. Mladenić, J. Brank, M. Grobelnik, and N. Milic-Frayling, “Feature selection using linear classifier weights: Interaction with classification models,” in Proceedings of Sheffield SIGIR—27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 234–241, Sheffield, UK, July 2004. View at Scopus
  18. K. Greer, “Symbolic neural networks for clustering higher-level concepts,” NAUN International Journal of Computers, vol. 5, no. 3, pp. 378–386, 2011. View at Google Scholar