Intelligent Water Drops Algorithm for Rough Set Feature Selection

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7803)


In this article; Intelligent Water Drops (IWD) algorithm is adapted for feature selection with Rough Set (RS). Specifically, IWD is used to search for a subset of features based on RS dependency as an evaluation function. The resulting system, called IWDRSFS (Intelligent Water Drops for Rough Set Feature Selection), is evaluated with six benchmark data sets. The performance of IWDRSFS are analysed and compared with those from other methods in the literature. The outcomes indicate that IWDRSFS is able to provide competitive and comparable results. In summary, this study shows that IWD is a useful method for undertaking feature selection problems with RS.


Feature Selection (FS) Rough Set (RS) Intelligent Water Drops (IWD) 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abdullah, S., Jaddi, N.: Great deluge algorithm for rough set attribute reduction. Database Theory and Application, Bio-Science and Bio-Technology, 189–197 (2010)Google Scholar
  2. 2.
    Blake, C., Merz, C.: {UCI} repository of machine learning databases (1998),
  3. 3.
    Dash, M., Liu, H.: Feature selection for classification. Intelligent data analysis 1(1-4), 131–156 (1997)CrossRefGoogle Scholar
  4. 4.
    Duan, H., Liu, S., Wu, J.: Novel intelligent water drops optimization approach to single ucav smooth trajectory planning. Aerospace Science and Technology 13(8), 442–449 (2009)CrossRefGoogle Scholar
  5. 5.
    Hedar, A., Wang, J., Fukushima, M.: Tabu search for attribute reduction in rough set theory. Soft Computing-A Fusion of Foundations, Methodologies and Applications 12(9), 909–918 (2008)zbMATHGoogle Scholar
  6. 6.
    Hendrawan, Y., Murase, H.: Neural-intelligent water drops algorithm to select relevant textural features for developing precision irrigation system using machine vision. Computers and Electronics in Agriculture 77(2), 214–228 (2011)CrossRefGoogle Scholar
  7. 7.
    Jensen, R.: A collection of datasets used in feature selection experimentation, (visited October 14, 2012)
  8. 8.
    Jensen, R., Shen, Q.: Finding rough set reducts with ant colony optimization. In: Proceedings of the 2003 UK Workshop on Computational Intelligence, vol. 1 (2003)Google Scholar
  9. 9.
    Jensen, R., Shen, Q.: Computational intelligence and feature selection: rough and fuzzy approaches, vol. 8. Wiley-IEEE Press (2008)Google Scholar
  10. 10.
    Kim, H., Howland, P., Park, H.: Dimension reduction in text classification with support vector machines. Journal of Machine Learning Research 6(1), 37 (2006)MathSciNetGoogle Scholar
  11. 11.
    Niu, S., Ong, S., Nee, A.: An improved intelligent water drops algorithm for achieving optimal job-shop scheduling solutions. International Journal of Production Research 50(15), 4192–4205 (2012)CrossRefGoogle Scholar
  12. 12.
    Pawlak, Z.: Rough sets. International Journal of Parallel Programming 11(5), 341–356 (1982)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Shah-Hosseini, H.: Intelligent water drops algorithm: A new optimization method for solving the multiple knapsack problem. International Journal of Intelligent Computing and Cybernetics 1(2), 193–212 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Shah-Hosseini, H.: An approach to continuous optimization by the intelligent water drops algorithm. Procedia-Social and Behavioral Sciences 32, 224–229 (2012)CrossRefGoogle Scholar
  15. 15.
    Shah-Hosseini, H.: Intelligent water drops algorithm for automatic multilevel thresholding of grey–level images using a modified otsu’s criterion. International Journal of Modelling, Identification and Control 15(4), 241–249 (2012)CrossRefGoogle Scholar
  16. 16.
    Shah-Hosseini, H.: Problem solving by intelligent water drops. In: IEEE Congress on Evolutionary Computation, CEC 2007, pp. 3226–3231. IEEE (2007)Google Scholar
  17. 17.
    Suguna, N., Thanushkodi, K.: A novel rough set reduct algorithm for medical domain based on bee colony optimization. Journal of Computing 2(6), 49–54 (2010)Google Scholar
  18. 18.
    Wang, J., Hedar, A., Zheng, G., Wang, S.: Scatter search for rough set attribute reduction. In: International Joint Conference on Computational Sciences and Optimization, CSO 2009, vol. 1, pp. 531–535. IEEE (2009)Google Scholar
  19. 19.
    Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. The Journal of Machine Learning Research 5, 1205–1224 (2004)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of Computer SciencesUniversiti Sains MalaysiaPinangMalaysia
  2. 2.Centre for Intelligent Systems ResearchDeakin UniversityAustralia
  3. 3.Department of Computer ScienceJadara UniversityIrbidJordan

Personalised recommendations