Non-binary classification trees

Statistics and Computing

Volumn 6, Issue 3, 1996, Pages 231-243

  Keprta, Stanislav ^a  

^a CHARLES UNIVERSITY   (Czech Republic)

Author keywords

Classification tree; Multi way splits; Tree growing; Tree pruning

Indexed keywords

EID: 27144507867     PISSN: 09603174     EISSN: None     Source Type: Journal    
DOI: 10.1007/BF00140867     Document Type: Article

References (20)

1. Andrews, D. F. and Herzberg, A. M. (1985) Data - A Collection of Problems from Many Fields for the Student and Research Worker. Springer-Verlag, New York.
2. Bahl, L. R., Brown, P. F., deSouza, P. V. and Mercer, R. L. (1989) A tree-based statistical language model for natural language speech recognition. IEEE Transactions on Acoustics, Speech, and Signal Proceeding, 37, 1001-8.
3. Biggs, D., de Ville, B. and Suen, E. (1991) A method for choosing multiway partitions for classification and decision trees. Journal of Applied Statistics, 18, 49-62.
4. Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J. (1984) Classification and Regression Trees. Wadsworth, Belmont, CA.
5. Buntine, W. L. (1993) Learning classification trees. Artificial Intelligence Frontiers in Statistics: AI and Statistics, III, 182-201.
6. Chou, P. A. (1991) Optimal partitioning for classification and regression trees. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 340-53.
7. Ciampi, A. (1993) Constructing prediction trees from data, the RECPAM approach. In: J. Antoch, ed., Computational Aspects of Model Choice. Physica-Verlag, Heidelberg.
8. Duran, B. S. and Odell, P. L. (1974) Cluster Analysis. Springer-Verlag Berlin.
9. Gelfand, S. B., Ravishankar, C. S. and Delp, E. J. (1991). An iterative growing and pruning algorithm for classification tree design. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 163-74.
10. Hawkins, D. M. (1990) FIRM (Formal Inference-based Recursive Modeling). Technical Report Number 546, University of Minnesota, School of Statistics.
11. Kass, G. V. (1980) An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 24, 119-27.
12. Keprta, S. (1993) Klasifkátory konstruované pomocí rekurzivních postupů. Master's thesis, Charles University Prague.
13. Mingers, J. (1989) Empirical comparison of selection measures for decision tree induction. Machine Learning, 3, 319-42.
14. Oliver, J. J., Dowe, D. L. and Wallace, C. S. (1992) Inferring decision graphs using the minimum message principle. In A. Adams and L. Sterling (eds) Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, 361-7. World Scientific, Singapore.
15. Quinlan, J. R. (1986) Induction of decision trees. Machine Learning, 1, 81-106.
16. Quinlan, J. R. (1993) C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA.
17. Quinlan, J. R. and Rivest, R. L. (1989) Inferring decision trees using the minimum description length principle. Information and Computation, 80, 227-48.
18. Safavian, S. R. and Landgrebe, D. (1991) A survey of decision tree classifiers. IEEE Transactions on Systems, Man, and Cybernetics, 21, 660-74.
19. Wallace, C. S. and Patrick, J. D. (1993) Coding decision trees. Machine Learning, 11, 7-22.
20. Wolfram, S. (1991) Mathematica: A System for Doing Mathematics by Computer. Addison-Wesley, Reading, MA.