Rank Aggregation Algorithm Selection Meets Feature Selection

  • Alexey Zabashta
  • Ivan Smetannikov
  • Andrey Filchenkov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9729)

Abstract

Rank aggregation is the important task in many areas, and different rank aggregation algorithms are created to find optimal rank. Nevertheless, none of these algorithms is the best for all cases. The main goal of this work is to develop a method, which for each rank list defines, which rank aggregation algorithm is the best for this rank list. Canberra distance is used as a metric for determining the optimal ranking. Three approaches are proposed in this paper and one of them has shown promising result. Also we discuss, how this approach can be applied to learn filtering feature selection algorithm ensemble.

Keywords

Meta-learning Rank aggregation Ensemble learning Feature selection 

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References

  1. 1.
    Albert, M.H., Aldred, R.E., Atkinson, M.D., van Ditmarsch, H.P., Handley, B., Handley, C.C., Opatrny, J.: Longest subsequences in permutations. Australasian Journal of Combinatorics 28, 225–238 (2003)MathSciNetMATHGoogle Scholar
  2. 2.
    Bachmaier, C., Brandenburg, F.J., Gleißner, A., Hofmeier, A.: On maximum rank aggregation problems. In: Lecroq, T., Mouchard, L. (eds.) IWOCA 2013. LNCS, vol. 8288, pp. 14–27. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Benner, P., Mehrmann, V., Sorensen, D.C.: Dimension Reduction of Large-Scale Systems, vol. 45. Springer, Heidelberg (2005)MATHGoogle Scholar
  4. 4.
    Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A., Benítez, J., Herrera, F.: A review of microarray datasets and applied feature selection methods. Information Sciences 282, 111–135 (2014)CrossRefGoogle Scholar
  5. 5.
    de Borda, J.C.: Mémoire sur les élections au scrutin (1781)Google Scholar
  6. 6.
    Brazdil, P., Carrier, C.G., Soares, C., Vilalta, R.: Metalearning: Applications to Data Mining. Springer Science & Business Media (2008)Google Scholar
  7. 7.
    Burkovski, A., Lausser, L., Kraus, J.M., Kestler, H.A.: Rank aggregation for candidate gene identification. In: Data Analysis, Machine Learning and Knowledge Discovery, pp. 285–293. Springer (2014)Google Scholar
  8. 8.
    Copeland, A.H.: A reasonable social welfare function. In: Seminar on Applications of Mathematics to Social Sciences. University of Michigan (1951)Google Scholar
  9. 9.
    Das, S., Das, A.K.: Sample classification based on gene subset selection. In: Behera, H.S., Mohapatra, D.P. (eds.) Computational Intelligence in Data Mining. AISC, vol. 410, pp. 227–236. Springer, India (2015)CrossRefGoogle Scholar
  10. 10.
    DeConde, R.P., Hawley, S., Falcon, S., Clegg, N., Knudsen, B., Etzioni, R.: Combining results of microarray experiments: a rank aggregation approach. Statistical Applications in Genetics and Molecular Biology 5(1) (2006)Google Scholar
  11. 11.
    Deza, M., Huang, T.: Metrics on permutations, a survey. Journal of Combinatorics, Information and System Sciences. Citeseer (1998)Google Scholar
  12. 12.
    Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  13. 13.
    Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the web. In: Proceedings of the 10th International Conference on World Wide Web, pp. 613–622. ACM (2001)Google Scholar
  14. 14.
    Filchenkov, A., Pendryak, A.: Datasets meta-feature description for recommending feature selection algorithm. In: AINL-ISMW FRUCT, pp. 11–18 (2015)Google Scholar
  15. 15.
    Fisher, R.A., Yates, F., et al.: Statistical tables for biological, agricultural and medical research. Statistical Tables for Biological, Agricultural and Medical Research 13(Ed. 6.) (1963)Google Scholar
  16. 16.
    Garner, S.R., et al.: Weka: the waikato environment for knowledge analysis. In: Proceedings of the New Zealand Computer Science Research Students Conference, pp. 57–64. Citeseer (1995)Google Scholar
  17. 17.
    Giraud-Carrier, C.: Metalearning-a tutorial. In: Proceedings of the 7th International Conference on Machine Learning and Applications, pp. 1–45 (2008)Google Scholar
  18. 18.
    Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.A.: Feature Extraction: Foundations and Applications, vol. 207. Springer (2008)Google Scholar
  19. 19.
    Jones, N.C., Pevzner, P.: An Introduction to Bioinformatics Algorithms. MIT press (2004)Google Scholar
  20. 20.
    Kekre, H.B., Shah, K.: Performance Comparison of Kekre’s Transform with PCA and Other Conventional Orthogonal Transforms for Face Recognition, pp. 873–879. ICETET (2009)Google Scholar
  21. 21.
    Kent, J.T.: Information gain and a general measure of correlation. Biometrika 70(1), 163–173 (1983)MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    Rice, J.R.: The Algorithm Selection Problem (1975)Google Scholar
  23. 23.
    Saeys, Y., Inza, I., Larranaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)CrossRefGoogle Scholar
  24. 24.
    Schalekamp, F., van Zuylen, A.: Rank aggregation: together we’re strong. In: Proceedings of the Meeting on Algorithm Engineering & Expermiments, pp. 38–51. Society for Industrial and Applied Mathematics (2009)Google Scholar
  25. 25.
    Smetannikov, I., Filchenkov, A.: Melif: filter ensemble learning algorithm forgene selection. In: Advanced Science Letters (2016, to appear)Google Scholar
  26. 26.
    Wang, G., Song, Q., Sun, H., Zhang, X., Xu, B., Zhou, Y.: A feature subset selection algorithm automatic recommendation method. Journal of Artificial Intelligence Research 47(1), 1–34 (2013)MATHGoogle Scholar
  27. 27.
    Wang, R., Utiyama, M., Goto, I., Sumita, E., Zhao, H., Lu, B.L.: Converting continuous-space language models into n-gram language models with efficient bilingual pruning for statistical machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing 15(3), 11 (2016)CrossRefGoogle Scholar
  28. 28.
    Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67–82 (1997)CrossRefGoogle Scholar
  29. 29.
    Zabashta, A., Smetannikov, I., Filchenkov, A.: Study on meta-learning approach application in rank aggregation algorithm selection. In: MetaSel Workshop at ECML PKDD 2015, pp. 115–117 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Alexey Zabashta
    • 1
  • Ivan Smetannikov
    • 1
  • Andrey Filchenkov
    • 1
  1. 1.Computer Science DepartmentITMO UniversitySt. PetersburgRussia

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