Inference based classifier: Efficient construction of decision trees for sparse categorical attributes

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2737, Issue , 2003, Pages 182-191

  Lo, Shih Hsiang ^a   Ou, Jian Chih ^a   Chen, Ming Syan ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; DATA WAREHOUSES; DECISION TREES; LEARNING SYSTEMS; TREES (MATHEMATICS);

CATEGORICAL ATTRIBUTES; CLASSIFICATION ACCURACY; DECISION TREE CLASSIFIERS; EFFICIENT CONSTRUCTION; INFORMATION GAIN; OVERFITTING; SPARSE DISTRIBUTION; SPLITTING NUMBER;

CLASSIFICATION (OF INFORMATION);

EID: 35248827754     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-45228-7_19     Document Type: Article

References (17)

1. L. Breiman, J. H. Friedman, R.A. Olshen, and C.J. Sotne. Classification and Regression Trees. Wadsworth, Belmont, 1984.
2. NASA Ames Research Center. Introduction to IND Version 2.1. GA23-2475-02 edition, 1992.
3. P. Chesseman, J. Kelly, M. Self, and et al. AutoClass: A Bayesian classification system. In 5th Int'l Conf. on Machine Learning. Morgan Kaufman, 1988.
4. P. A. Chou. Optimal Partitioning for Classification and Resgression Trees. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 13, No 4, 1991.
5. U. Fayyad. On the Induction of Decision Trees for multiple Concept Learning. PhD thesis, The University of Michigan, Ann arbor, 1991.
6. U. Fayyad and K. B. Irani. Multi-interval discretization of continuous-valued attributes for classification learning. In Proc. of the 13th International Joint Conference on Artificial Intelligence, 1993.
7. D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Morgan Kaufmann, 1989.
8. J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2000.
9. M. Mehta, R. Agrawal, and J. Rissanen. SLIQ: A fast scalable classifier for data mining. In EDBT 96, Avignonm, France, 1996.
10. M. Mehta, J. Rissanen, and R. Agrawal. MDL-based decision tree pruning. In Int'l Conference on Knowledge Discovery in Databases and Data Mining, 1995.
11. D. Michie, D.J. Spiegelhalter, and C.C. Taylor. Machine Learning, Neural and Statistical Classification. Ellis Horwood, 1994.
12. J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
13. J.R. Quinlan. Induction of decision trees. Machine Learning, 1986.
14. J.R. Quinlan and R. L. Rivest. Inferring decision trees using minimum description length principle. Information and Computtation, 1989.
15. R. Rastogi and K. Shim. PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning. Proceedings of 24rd International Conference on Very Large Data Bases, August 24-27, 1998, New York City, New York, USA, 1998.
16. B. D. Ripley. Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge, 1996.
17. J. Shafer, R. Agrawal, and M. Mehta. SPRINT: A scalable parallel classifier for data mining. In Proc. of the VLDB Conference, Bombay, India, 1996.