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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2017: Advances in Knowledge Discovery and Data Mining pp 208–219Cite as

Partial Tree-Edit Distance: A Solution to the Default Class Problem in Pattern-Based Tree Classification

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10235))

Abstract

Pattern-based tree classifiers are capable of producing high quality results, however, they are prone to the problem of the default class overuse. In this paper, we propose a measure designed to address this issue, called partial tree-edit distance (PTED), which allows for assessing the degree of containment of one tree in another. Furthermore, we propose an algorithm which calculates the measure and perform an experiment involving pattern-based classification to illustrate its usefulness. The results show that incorporating PTED into the classification scheme allowed us to significantly improve the accuracy on the tested datasets.

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References

  1. Hachicha, M., Darmont, J.: A survey of XML tree patterns. IEEE Trans. Knowl. Data Eng. 25(1), 29–46 (2013)

    Article  Google Scholar 

  2. Dulucq, S., Tichit, L.: RNA secondary structure comparison: exact analysis of the Zhang-Shasha tree edit algorithm. Theor. Comput. Sci. 306(1–3), 471–484 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  3. Kouylekov, M., Magnini, B.: Combining lexical resources with tree edit distance for recognizing textual entailment. In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds.) MLCW 2005. LNCS (LNAI), vol. 3944, pp. 217–230. Springer, Heidelberg (2006). doi:10.1007/11736790_12

    Chapter  Google Scholar 

  4. Augsten, N., Bohlen, M., Dyreson, C., Gamper, J.: Approximate joins for data-centric XML. In: IEEE 24th International Conference on Data Engineering, ICDE 2008, pp. 814–823 (2008)

    Google Scholar 

  5. Augsten, N., Barbosa, D., Bohlen, M., Palpanas, T.: Efficient top-k approximate subtree matching in small memory. IEEE Trans. Knowl. Data Eng. 23(8), 1123–1137 (2011)

    Article  Google Scholar 

  6. Zaki, M.J., Aggarwal, C.C.: XRules: an effective algorithm for structural classification of XML data. Mach. Learn. 62(1–2), 137–170 (2006)

    Article  Google Scholar 

  7. Stefanowski, J.: Algorithms of rule induction for knowledge discovery. Habilitation thesis (2001)

    Google Scholar 

  8. Zhang, K., Shasha, D.: Simple fast algorithms for the editing distance between trees and related problems. SIAM J. Comput. 18(6), 1245–1262 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  9. Cohen, S., Or, N.: A general algorithm for subtree similarity-search. In: Proceedings of the 30th International Conference on Data Engineering, ICDE 2014, pp. 928–939 (2014)

    Google Scholar 

  10. Cohen, S.: Indexing for subtree similarity-search using edit distance. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, SIGMOD 2013, pp. 49–60 (2013)

    Google Scholar 

  11. Amer-Yahia, S., Cho, S.R., Srivastava, D.: Tree pattern relaxation. In: Jensen, C.S., Šaltenis, S., Jeffery, K.G., Pokorny, J., Bertino, E., Böhn, K., Jarke, M. (eds.) EDBT 2002. LNCS, vol. 2287, pp. 496–513. Springer, Heidelberg (2002). doi:10.1007/3-540-45876-X_32

    Chapter  Google Scholar 

  12. Zhang, K., Shasha, D., Wang, J.T.L.: Approximate tree matching in the presence of variable length don’t cares. J. Algorithms 16, 33–66 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  13. Levenshtein, V.: Binary codes capable of correcting deletions, insertions and reversals. Sov. Phys. Dokl. 10, 707 (1966)

    MathSciNet  MATH  Google Scholar 

  14. Zaki, M.J.: Efficiently mining frequent trees in a forest: algorithms and applications. IEEE Trans. Knowl. Data Eng. 17(8), 1021–1035 (2005)

    Article  Google Scholar 

  15. Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

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Acknowledgments

This research is partly funded by the Polish National Science Center under Grant No. 2015/19/B/ST6/02637.

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Authors and Affiliations

  1. Institute of Computing Science, Poznan University of Technology, ul. Piotrowo 2, 60-965, Poznan, Poland

    Maciej Piernik & Tadeusz Morzy

Authors
  1. Maciej Piernik
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  2. Tadeusz Morzy
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Corresponding author

Correspondence to Maciej Piernik .

Editor information

Editors and Affiliations

  1. Kangwon National University, Chuncheon, Korea (Republic of)

    Jinho Kim

  2. Seoul National University, Seoul, Korea (Republic of)

    Kyuseok Shim

  3. University of Technology Sydney, Sydney, New South Wales, Australia

    Longbing Cao

  4. KAIST, Daejeon, Korea (Republic of)

    Jae-Gil Lee

  5. University of New South Wales, Sydney, New South Wales, Australia

    Xuemin Lin

  6. Kangwon National University, Chuncheon, Korea (Republic of)

    Yang-Sae Moon

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Piernik, M., Morzy, T. (2017). Partial Tree-Edit Distance: A Solution to the Default Class Problem in Pattern-Based Tree Classification. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10235. Springer, Cham. https://doi.org/10.1007/978-3-319-57529-2_17

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  • DOI: https://doi.org/10.1007/978-3-319-57529-2_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57528-5

  • Online ISBN: 978-3-319-57529-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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