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Performance Evaluation of Decision Tree Graph-Based Induction

  • Warodom Geamsakul
  • Takashi Matsuda
  • Tetsuya Yoshida
  • Hiroshi Motoda
  • Takashi Washio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2843)

Abstract

A machine learning technique called Decision tree Graph-Based Induction (DT-GBI) constructs a classifier (decision tree) for graph-structured data, which are usually not explicitly expressed with attribute-value pairs. Substructures (patterns) are extracted at each node of a decision tree by stepwise pair expansion (pairwise chunking) in GBI and they are used as attributes for testing. DT-GBI is efficient since GBI is used to extract patterns by greedy search and the obtained result (decision tree) is easy to understand. However, experiments against a DNA dataset from UCI repository revealed that the predictive accuracy of the classifier constructed by DT-GBI was not high enough compared with other approaches. Improvement is made on its predictive accuracy and the performance evaluation of the improved DT-GBI is reported against the DNA dataset. The predictive accuracy of a decision tree is affected by which attributes (patterns) are used and how it is constructed. To extract good enough discriminative patterns, search capability is enhanced by incorporating a beam search into the pairwise chunking within the greedy search framework. Pessimistic pruning is incorporated to avoid overfitting to the training data. Experiments using a DNA dataset were conducted to see the effect of the beam width, the number of chunking at each node of a decision tree, and the pruning. The results indicate that DT-GBI that does not use any prior domain knowledge can construct a decision tree that is comparable to other classifiers constructed using the domain knowledge.

Keywords

Decision Tree Domain Knowledge Predictive Accuracy Information Gain Beam Width 
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.
    Blake, C.L., Keogh, E., Merz, C.J.: Uci repository of machine leaning database (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
  2. 2.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth & Brooks/Cole Advanced Books & Software (1984)Google Scholar
  3. 3.
    Matsuda, T., Horiuchi, T., Motoda, H., Washio, T.: Extension of graph-based induction for general graph structured data. In: Terano, T., Chen, A.L.P. (eds.) PAKDD 2000. LNCS (LNAI), vol. 1805, pp. 420–431. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  4. 4.
    Matsuda, T., Motoda, H., Yoshida, T., Washio, T.: Knowledge discovery from structured data by beam-wise graph-based induction. In: Ishizuka, M., Sattar, A. (eds.) PRICAI 2002. LNCS (LNAI), vol. 2417, pp. 255–264. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Matsuda, T., Yoshida, T., Motoda, H., Washio, T.: Mining patterns from structured data by beam-wise graph-based induction. In: Lange, S., Satoh, K., Smith, C.H. (eds.) DS 2002. LNCS, vol. 2534, pp. 422–429. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)Google Scholar
  7. 7.
    Quinlan, J.R.: C4.5:Programs For Machine Learning. Morgan Kaufmann Publishers, San Francisco (1993)Google Scholar
  8. 8.
    Towell, G.G., Shavlik, J.W.: Extracting refined rules from knowledge-based neural networks. Machine Learning 13, 71–101 (1993)Google Scholar
  9. 9.
    Warodom, G., Matsuda, T., Yoshida, T., Motoda, H., Washio, T.: Classifier construction by graph-based induction for graph-structured data. In: PAKDD 2003. LNCS (LNAI), vol. 2637, pp. 52–62. Springer, Heidelberg (2003)Google Scholar
  10. 10.
    Yoshida, K., Motoda, H.: Clip: Concept learning from inference pattern. Journal of Artificial Intelligence 75(1), 63–92 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Warodom Geamsakul
    • 1
  • Takashi Matsuda
    • 1
  • Tetsuya Yoshida
    • 1
  • Hiroshi Motoda
    • 1
  • Takashi Washio
    • 1
  1. 1.Institute of Scientific and Industrial ResearchOsaka UniversityOsakaJAPAN

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