Extracting decision trees from trained neural networks

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2002, Pages 456-461

  Boz, Olcay ^a  

^a AT AND T LABS RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA MINING; DATA STRUCTURES; DECISION THEORY; EXPERT SYSTEMS; TREES (MATHEMATICS);

DECISION TREES; PRUNING;

NEURAL NETWORKS;

EID: 0242625262     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/775107.775113     Document Type: Conference Paper

References (10)

1. L. Atlas, R. Cole, J. Connor, M. El-Sharkawi, R.J. Marks, V. Muthusamy, and E. Barnard. Performance comparisons between backpropagation networks and classification trees on three real-world applications. In Advances in Neural Information Processing Systems, volume 2, pages 622-629, 1990.
2. O. Boz. Converting A Trained Neural Network To A Decision Tree DecText - Decision Tree Extractor. PhD thesis, Computer Science and Engineering, Lehigh University 2000.
3. W. Buntine and T. Niblett. A further comparison of splitting rules for decision-tree induction. Machine Learning, 8:75, 1992.
4. M.W. Craven. Extracting Comprehensible Models from Trained Neural Networks. PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison, 1996. (Also appears as UW Technical Report CS-TR-96-1326).
5. M.W. Craven and J.W. Shavlik. Extracting tree-structured representations of trained networks. In Advances in Neural Information Processing Systems, volume 8, pages 24-30, Denver, CO, 1996. MIT Press.
6. D. Fisher and K. McKusick. An emprical comparison of id3 and back propagation. In Proceedings of the eleventh international Joint Conference on Artificial Intelligence, pages 788 - 793, Detroit, 1989. Morgan Kaufmann.
7. P. Murphy and M. Pazzani. Id2-of-3: Constructive induction of n-of-m concepts for discriminators in decision trees. In Proceedings of the Eighth International Machine learning Workshop, pages 183-187, Evanston, IL, 1991. Morgan Kaufmann.
8. J.W. Shavlik, R.J. Mooney, and G.G. Towell. Symbolic and neural learning algorithms: An experimental comparison. Machine Learning, 6:111-143, 1991.
9. B.W. Silverman. Density Estimation for Statistics and Data Analysis. Champman & Hall, London, 1986.
10. S.M. Weiss and I. Kapouleas. An empirical comparison of pattern recognition, neural nets, and machine learning classification methods. In J. W. Shavlik and T. G. Dietterich, editors, Readings in Machine Learning. Morgan Kaufman, San Mateo, CA, 1990.