Probabilistic inference trees for classification and ranking

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

Volumn 4013 LNAI, Issue , 2006, Pages 526-537

  Su, Jiang ^a   Zhang, Harry ^a  

^a UNIVERSITY OF NEW BRUNSWICK   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTATION THEORY; DECISION THEORY; LEARNING ALGORITHMS; PROBABILISTIC LOGICS; PROBABILITY;

DECISION TREES; PROBABILISTIC INFERENCE TREES (PIT); PROBABILITY THEORY; RANKING;

TREES (MATHEMATICS);

EID: 33746089538     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11766247_45     Document Type: Conference Paper

References (15)

1. Bauer, E., Kohavi, R.: An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning, 38(1-2) 1999 105-139
2. Buntine, W.: Learning Classification Trees. Artificial Intelligence Frontiers in Statistics. Chapman Hall, London (1991) 182-201
3. Ferri, C., Flach, P. A., Hernndez-Orallo, J.: Improving the AUC of Probabilistic Estimation Trees. Proceedings of 14th European Conference on Machine Learning. Springer (2003) 121-132
4. Hand, D. J., Till, R. J.: A simple generalisation of the area under the ROC curve for multiple class classification problems. Machine Learning 45 (2001) 171-186
5. Kohavi, R.: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96). AAAI Press (1996) 202-207
6. Ling, C. X., Yan, R. J.: Decision Tree with Better Ranking. Proceedings of the 20th International Conference on Machine Learning. Morgan Kaufmann (2003) 480-487
7. Pazzani, M., Merz, C., Murphy, P., Ali, K., Hume, T., Brunk, C.: Reducing misclassification costs. Proceedings of the 11th International conference on Machine Learning. Morgan Kaufmann (1994) 217-225
8. Provost, F., Fawcett, T.: Analysis and visualization of classifier performance: comparison under imprecise class and cost distribution. Proceedings of the Third International Conference on Knowledge Discovery and Data Mining. AAAI Press (1997) 43-48
9. Provost, F., Fawcett, T., Kohavi, R.: The case against accuracy estimation for comparing induction algorithms. Proceedings of the Fifteenth International Conference on Machine Learning. Morgan Kaufmann (1998) 445-453
10. Provost, F. J., Domingos, P.: Tree Induction for Probability-Based Ranking. Machine Learning 52(3) (2003) 199-215
11. Quinlan, J. R.: C4.5: Programs for Machine Learning. Morgan Kaufmann: San Mateo, CA (1993)
12. Symth, P., Gray, A., Fayyad, U.: Retrofitting decision tree classifiers using kernel density estimation. Proceedings of the Twelfth International Conference on Machine Learning. Morgan Kaufmann (1996) 506-514
13. Su, J., Zhang, H.: Representing Conditional Independence Using Decision Trees. Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-05). AAAI Press(2005) 874-879.
14. Witten, I. H., Frank, E.: data mining-Practical Machine Learning Tools and Techniques with Java Implementation. Morgan Kaufmann (2000)
15. Zhang, H., Su, J.: Conditional Independence Trees. Proceedings of the 15th European Conference on Machine Learning (ECML2004), Springer(2004) 513-524