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Weighted Pattern Trees: A Case Study with Customer Satisfaction Dataset

  • Zhiheng Huang
  • Masoud Nikravesh
  • Ben Azvine
  • Tamás D. Gedeon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4529)

Abstract

A pattern tree [1] is a tree which propagates fuzzy terms using different fuzzy aggregations. Each pattern tree represents a structure for an output class in the sense that how the fuzzy terms aggregate to predict such a class. Unlike decision trees, pattern trees explicitly make use of t-norms (i.e., AND) and t-conorms (OR) to build trees, which is essential for applications requiring rules connected with t-conorms explicitly. Pattern trees can not only obtain high accuracy rates in classification applications, but also be robust to over-fitting. This paper further extends pattern trees approach by assigning certain weights to different trees, to reflect the nature that different trees may have different confidences. The concept of weighted pattern trees is important as it offers an option to trade off the complexity and performance of trees. In addition, it enhances the semantic meaning of pattern trees. The experiments on British Telecom (BT) customer satisfaction dataset show that weighted pattern trees can slightly outperform pattern trees, and both of them are slightly better than fuzzy decision trees in terms of prediction accuracy. In addition, the experiments show that (weighted) pattern trees are robust to over-fitting. Finally, a limitation of pattern trees as revealed via BT dataset analysis is discussed and the research direction is outlined.

Keywords

Root Mean Square Error Prediction Accuracy Pattern Tree Output Class High Prediction Accuracy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Huang, Z.H., Gedeon, T.D.: Pattern trees. In: IEEE International Conference on Fuzzy Systems, pp. 1784–1791 (2006)Google Scholar
  2. 2.
    Huang, Z.H., Gedeon, T.D., Nikravesh, M.: Pattern trees. Submitted to IEEE Transactions on Fuzzy Systems (2006)Google Scholar
  3. 3.
    Kóczy, L.T., Vámos, T., Biró, G.: Fuzzy signatures. In: EUROFUSE-SIC, pp. 210–217 (1999)Google Scholar
  4. 4.
    Mendis, B.S.U., Gedeon, T.D., Kóczy, L.T.: Investigation of aggregation in fuzzy signatures. In: 3rd International Conference on Computational Intelligence, Robotics and Autonomous Systems (2005)Google Scholar
  5. 5.
    Nikravesh, M.: Soft computing for perception-based decision processing and analysis: web-based BISC-DSS. In: Soft Computing for Information Processing and Analysis. Studies in Fuzziness and Soft Computing, vol. 164, pp. 93–188. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Quinlan, J.R.: Decision trees and decision making. IEEE Transactions on Systems, Man, and Cybernetics 20(2), 339–346 (1994)CrossRefGoogle Scholar
  7. 7.
    Schweizer, B., Sklar, A.: Associative functions and abstract semigroups. Publ. Math. Debrecen 10, 69–81 (1963)MathSciNetGoogle Scholar
  8. 8.
    Yager, R.R.: On ordered weighted averaging aggregation operators in multicritera decison making. IEEE Transactions on Systems, Man and Cybernetics 18, 183–190 (1988)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Yuan, Y., Shaw, M.J.: Induction of fuzzy decision trees. Fuzzy Sets and Systems 69(2), 125–139 (1995)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Zhiheng Huang
    • 1
  • Masoud Nikravesh
    • 1
  • Ben Azvine
    • 2
  • Tamás D. Gedeon
    • 3
  1. 1.Electrical Engineering and Computer Science, University of California at Berkeley, CA 94720USA
  2. 2.Computational Intelligence Research Group, Intelligent Systems Research Center, BT Group Chief Technology Office, British Telecom 
  3. 3.Department of Computer Science, The Australian National University, Canberra, ACT 0200Australia

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