Any-cost discovery: Learning optimal classification rules

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

Volumn 3809 LNAI, Issue , 2005, Pages 123-132

  Ni, Ailing ^a   Zhu, Xiaofeng ^a   Zhang, Chengqi ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SCIENCE; DATABASE SYSTEMS; DIAGNOSIS; LEARNING SYSTEMS; TREES (MATHEMATICS);

DATASET; DECISION TREE; MISCLASSIFICATION COST;

DECISION THEORY;

EID: 33745616561     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11589990_15     Document Type: Conference Paper

References (8)

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3. "Missing is Useful": Missing Values in Cost-sensitive Decision Trees. IEEE Transactions on Knowledge and Data Engineering, Shichao Zhang, Zhenxing Qin, Charles X.Ling, Shengli Sheng
4. Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm, Turney, P. D. Journal of Artificial Intelligence Research. 2, (1995): 369-409.
5. Decision Trees with Minimal Costs. Charles X. Ling, Qiang Yang, Jianning Wang, Shichao Zhang. Proceedings of the 21th International Conference on Machine Learning(ICML), Banff, Canada, 2004.
6. th ICML-2000 Conference, Standford, CA.
7. Pruning improves heuristic search for cost-sensitive learning. Zubek, V.B., and Dietterich, T. 2002. In Proceedings of the Nineteenth International Conference of Machine Learning, 20-35,Sydney, Australia; Morgan Kaufmann.
8. Learning Cost-Sensitive Active Classifiers. Russell Greiner, Adam J. Grove. Artificial Intelligence, Volume: 139, Issue: 2, August, 2002.