Comparison between Record to Record Travel and Great Deluge Attribute Reduction Algorithms for Classification Problem

  • Majdi Mafarja
  • Salwani Abdullah
Part of the Communications in Computer and Information Science book series (CCIS, volume 378)

Abstract

In this paper, two single-solution-based meta-heuristic methods for attribute reduction are presented. The first one is based on a record-to-record travel algorithm, while the second is based on a Great Deluge algorithm. These two methods are coded as RRT and m-GD, respectively. Both algorithms are deterministic optimisation algorithms, where their structures are inspired by and resemble the Simulated Annealing algorithm, while they differ in the acceptance of worse solutions. Moreover, they belong to the same family of meta-heuristic algorithms that try to avoid stacking in the local optima by accepting non-improving neighbours. The obtained reducts from both algorithms were passed to ROSETTA and the classification accuracy and the number of generated rules are reported. Computational experiments confirm that RRT m-GD is able to select the most informative attributes which leads to a higher classification accuracy.

Keywords

Record to Record Travel algorithm Great Deluge algorithm Rough Set Theory Classification 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zhang, D., Chen, S., Zhou, Z.H.: Constraint Score: A new filter method for feature selection with pairwise constraints. Pattern Recognition 41, 1440–1451 (2008)MathSciNetMATHCrossRefGoogle Scholar
  2. 2.
    Hu, Q., Zhu, P., Liu, J., Yang, Y., Yu, D.: Feature Selection via Maximizing Fuzzy Dependency. Fundamenta Informaticae 98, 167–181 (2010)MathSciNetMATHGoogle Scholar
  3. 3.
    Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, Boston (1998)MATHCrossRefGoogle Scholar
  4. 4.
    Langley, P.: Selection of relevant features in machine learning. In: Proceedings of the AAAI Fall Symposium on Relevance, pp. 1–5 (1994)Google Scholar
  5. 5.
    Pawlak, Z.: Rough Sets. International Journal of Information and Computer Sciences 11, 341–356 (1982)MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    Pawlak, Z.: Rough sets: Theoretical aspects of reasoning about data. Springer (1991)Google Scholar
  7. 7.
    Swiniarski, R.W., Skowron, A.: Rough set methods in feature selection and recognition. Pattern Recog. Lett. 24, 833–849 (2003)MATHCrossRefGoogle Scholar
  8. 8.
    Liu, H., Motoda, H., Yu, L.: Feature selection with selective sampling, pp. 395–402 (2002)Google Scholar
  9. 9.
    Liang, J., Wang, F., Dang, C., Qian, Y.: An efficient rough feature selection algorithm with a multi-granulation view. International Journal of Approximate Reasoning 53, 912–926 (2012)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Kabir, M.M., Shahjahan, M., Murase, K.: A new hybrid ant colony optimization algorithm for feature selection. Expert Syst. Appl. 39, 3747–3763 (2012)CrossRefGoogle Scholar
  11. 11.
    Deng, T., Yang, C., Wang, X.: A Reduct Derived from Feature Selection. Pattern Recog. Lett. 33, 1638–1646 (2012)CrossRefGoogle Scholar
  12. 12.
    Kaneiwa, K., Kudo, Y.: A sequential pattern mining algorithm using rough set theory. International Journal of Approximate Reasoning 52, 881–893 (2011)CrossRefGoogle Scholar
  13. 13.
    Kabir, M.M., Shahjahan, M., Murase, K.: A new local search based hybrid genetic algorithm for feature selection. Neurocomputing 74, 2914–2928 (2011)CrossRefGoogle Scholar
  14. 14.
    Anaraki, J.R., Eftekhari, M.: Improving fuzzy-rough quick reduct for feature selection. In: 19th Iranian Conference on Electrical Engineering (ICEE), pp. 1–6 (2011)Google Scholar
  15. 15.
    Suguna, N., Thanushkodi, K.: A Novel Rough Set Reduct Algorithm for Medical Domain Based on Bee Colony Optimization. Journal of Computing 2, 49–54 (2010)Google Scholar
  16. 16.
    Liu, H., Abraham, A., Li, Y.: Nature inspired population-based heuristics for rough set reduction. In: Abraham, A., Falcón, R., Bello, R. (eds.) Rough Set Theory. SCI, vol. 174, pp. 261–278. Springer, Heidelberg (2009)Google Scholar
  17. 17.
    Wroblewski, J.: Finding minimal reducts using genetic algorithms, pp. 186–189Google Scholar
  18. 18.
    Jensen, R., Shen, Q.: Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches. IEEE Trans. on Knowl. and Data Eng. 16, 1457–1471 (2004)CrossRefGoogle Scholar
  19. 19.
    Handels, H., Roß, T., Kreusch, J., Wolff, H.H., Pöppl, S.J.: Feature selection for optimized skin tumor recognition using genetic algorithms. Artif. Intell. Med. 16, 283–297 (1999)CrossRefGoogle Scholar
  20. 20.
    Wang, X., Yang, J., Teng, X., Xia, W., Jensen, R.: Feature selection based on rough sets and particle swarm optimization. Pattern Recog. Lett. 28, 459–471 (2007)CrossRefGoogle Scholar
  21. 21.
    Jensen, R., Shen, Q.: Finding Rough Set Reducts with Ant Colony Optimization. In: Proceedings of the 2003 UK Workshop on Computational Intelligence, pp. 15–22 (2003)Google Scholar
  22. 22.
    Ke, L., Feng, Z., Ren, Z.: An efficient ant colony optimization approach to attribute reduction in rough set theory. Pattern Recog. Lett. 29, 1351–1357 (2008)CrossRefGoogle Scholar
  23. 23.
    Hedar, A.-R., Wang, J., Fukushima, M.: Tabu search for attribute reduction in rough set theory. Soft Computing - A Fusion of Foundations, Methodologies and Applications 12, 909–918 (2006)Google Scholar
  24. 24.
    Abdullah, S., Jaddi, N.S.: Great Deluge Algorithm for Rough Set Attribute Reduction. In: Zhang, Y., Cuzzocrea, A., Ma, J., Chung, K.-I., Arslan, T., Song, X. (eds.). CCIS, vol. 118, pp. 189–197. Springer, Heidelberg (2010)Google Scholar
  25. 25.
    Jihad, S.K., Abdullah, S.: Investigating composite neighbourhood structure for attribute reduction in rough set theory. In: 10th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 1015–1020 (2010)Google Scholar
  26. 26.
    Arajy, Y.Z., Abdullah, S.: Hybrid variable neighbourhood search algorithm for attribute reduction in Rough Set Theory. In: Intelligent Systems Design and Applications (ISDA), pp. 1015–1020 (2010)Google Scholar
  27. 27.
    Abdullah, S., Sabar, N.R., Nazri, M.Z.A., Turabieh, H., McCollum, B.: A constructive hyper-heuristics for rough set attribute reduction. In: Intelligent Systems Design and Applications (ISDA), pp. 1032–1035 (2010)Google Scholar
  28. 28.
    Alomari, O., Othman, Z.A.: Bees Algorithm for feature selection in Network Anomaly detection. Journal of Applied Sciences Research 8, 1748–1756 (2012)Google Scholar
  29. 29.
    Dueck, G.: New Optimization Heuristics the Great Deluge Algorithm and the Record-to-Record Travel. Journal of Computational Physics 104, 86–92 (1993)MATHCrossRefGoogle Scholar
  30. 30.
    Talbi, E.G.: Metaheuristics from design to implementation. Wiley Online Library (2009)Google Scholar
  31. 31.
    Mafarja, M., Abdullah, S.: Modified great deluge for attribute reduction in rough set theory. In: Fuzzy Systems and Knowledge Discovery (FSKD), pp. 1464–1469 (2011)Google Scholar
  32. 32.
    Øhrn, A.: Discernibility and rough sets in medicine: tools and applications. Department of Computer and Information Science, PhD, p. 239. Norwegian University of Science and Technology, Trondheim, Norway (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Majdi Mafarja
    • 1
    • 2
  • Salwani Abdullah
    • 1
  1. 1.Data Mining and Optimization Research Group (DMO), Center for Artificial Intelligence TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia
  2. 2.Department of Computer Science, Faculty of Information TechnologyBirzeit UniversityBirzeitPalestine

Personalised recommendations