[Elkan01] Charles Elkan: The Foundations of Cost-Sensitive Learning. IJCAI 2001: 973-978.

[ZE01] Bianca Zadrozny, Charles Elkan: Learning and making decisions when costs and probabilities are both unknown. KDD 2001: 204-213

= =

[SL07] Victor S. Sheng, Charles X. Ling: Partial example acquisition in cost-sensitive learning. KDD 2007: 638-646

Jun Du, Zhihua Cai, Charles X. Ling: Cost-Sensitive Decision Trees with Pre-pruning. Canadian Conference on AI 2007: 171-179

 

Shengli Sheng, Charles X. Ling, Ailing Ni, Shichao Zhang: Cost-Sensitive Test Strategies. AAAI 2006

 

Victor S. Sheng, Charles X. Ling: Thresholding for Making Classifiers Cost-sensitive. AAAI 2006

 

Shengli Sheng, Charles X. Ling, Qiang Yang: Simple Test Strategies for Cost-Sensitive Decision Trees. ECML 2005: 365-376

 

Shengli Sheng, Charles X. Ling: Hybrid Cost-Sensitive Decision Tree. PKDD 2005: 274-284

 

Xiaoyong Chai, Lin Deng, Qiang Yang, Charles X. Ling: Test-Cost Sensitive Naive Bayes Classification. ICDM 2004: 51-58

 

 

C.X. Ling and S. Sheng. A Comparative Study of Cost-Sensitive Classifiers. Chinese Journal of Computers, 30(8), pp 1203-1212, 2007. (pdf)

 

Q. Yang., C.X. Ling, X. Chai, and R. Pan. Test-Cost Sensitive Classification on Data with Missing Values. IEEE Transactions on Knowledge and Data Engineering (TKDE), Volume 18, Number 5, 2006. Pages: 626 - 638.

 

Shichao Zhang, Zhenxing Qin, Charles X. Ling, Shengli Sheng. "Missing is Useful": Missing Values in Cost-sensitive Decision Trees. IEEE Transactions on Knowledge and Data Engineering (TKDE), Volume 17 , Issue 12, 1689-1693, 2005. (pdf file)

 

[Ting00ICML] Kai Ming Ting: A Comparative Study of Cost-Sensitive Boosting Algorithms. ICML 2000: 983-99.

[Ting00ECML] Kai Ming Ting: An Empirical Study of MetaCost Using Boosting Algorithms. ECML 2000: 413-425

[Turney95JAIR] Turney, P.D.: Cost-sensitive classification: empirical evaluation of a hybrid genetic decision tree induction algorithm. Journal of Artificial Intelligence Research 2, 369–409 (1995)
==> design cost-sensitive learning algorithms directly

Lizotte, D., Madani, O., Greiner, R.: Budgeted learning of naïve-Bayes classifiers. In: Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, Acapulco, Mexico (2003)

[Ting98PKDD] Ting, K.M.: Inducing cost-sensitive trees via instance weighting. In: Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery, pp. 23–26. Springer, Heidelberg (1998)
==> .....  algorithm
==> cost-sensitive meta-learning approach: weighting

[WP03JAIR] Weiss, G., Provost, F.: Learning when training data are costly: the effect of class distribution on tree induction. Journal of Artificial Intelligence Research 19, 315–354 (2003)

 

Xingquan Zhu, Xindong Wu, Taghi M. Khoshgoftaar, Yong Shi: An Empirical Study of the Noise Impact on Cost-Sensitive Learning. IJCAI 2007: 1168-1174

[DH00ICML] Drummond, C., Holte, R.: Exploiting the cost (in)sensitivity of decision tree splitting criteria. In: Proceedings of the 17th International Conference on Machine Learning, pp. 239–246 (2000)

==> design cost-sensitive learning algorithms directly approach

[LYWZ04ICML]Ling, C.X., Yang, Q., Wang, J., Zhang, S.: Decision trees with minimal costs. In: Proceedings of the Twenty-First International Conference on Machine Learning, Morgan Kaufmann, Banff, Alberta (2004)
==> design cost-sensitive learning algorithms directly approach

Domingos, P.: MetaCost: A general method for making classifiers cost-sensitive. In: Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, pp. 155–164. ACM Press, New York (1999)
==> MetaCost algorithm
==> cost-sensitive meta-learning approach: relabeling training instances

[ZLA03ICDM] Zadrozny, B., Langford, J., Abe, N.: Cost-sensitive learning by cost-proportionate instance weighting. In: Proceedings of the 3th International Conference on Data Mining (2003)
==> Costing algorithm
==> cost-sensitive meta-learning approach

Witten, I.H., Frank, E.: Data Mining – Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers, San Francisco (2005)
==> CostSensitiveClassifier (CSC) algorithm
==> cost-sensitive meta-learning approach: relabeling test instances

[Ting02] Kai Ming Ting: A Study on the Effect of Class Distribution Using Cost-Sensitive Learning. Discovery Science 2002: 98-112

[Ting02ICML] Kai Ming Ting: Issues in Classifier Evaluation using Optimal Cost Curves. ICML 2002: 642-649.

[Ting02TKDE] Kai Ming Ting: An Instance-Weighting Method to Induce Cost-Sensitive Trees. IEEE Trans. Knowl. Data Eng. (TKDE) 14(3): 659-665 (2002)

Mahesh V. Joshi, Ramesh C. Agarwal, Vipin Kumar: Predicting Rare Classes: Comparing Two-Phase Rule Induction to Cost-Sensitive Boosting. PKDD 2002: 237-249

 

Gary Weiss, Kate McCarthy, Bibi Zabar: Cost-Sensitive Learning vs. Sampling: Which is Best for Handling Unbalanced Classes with Unequal Error Costs? DMIN 2007: 35-41

Xingquan Zhu, Xindong Wu: Class Noise Handling for Effective Cost-Sensitive Learning by Cost-Guided Iterative Classification Filtering. IEEE Trans. Knowl. Data Eng. (TKDE) 18(10): 1435-1440 (2006)

 

Yanmin Sun, Mohamed S. Kamel, Andrew K. C. Wong, Yang Wang: Cost-sensitive boosting for classification of imbalanced data. Pattern Recognition 40(12): 3358-3378 (2007)

 

Pedro Domingos: Research Directions in MetaCost (abstract, gzipped ps)

Charles Elkan: Cost-Sensitive Learning and Decision-Making When Costs Are Unknown (gzipped ps)

Joao Gama: A Cost-Sensitive Iterative Bayes (gzipped ps)

Peter Turney: Types of Cost in Inductive Concept Learning (pdf)

gives an excellent survey on a variety of costs that may be considered in learning, such as misclassification costs, data acquisition cost (including example costs and attribute costs), active learning costs, computation cost, human-computer interaction cost, and so on.

[Victor S. Sheng, Charles X. L]

 

Kai Ming Ting: Cost-Sensitive Classification using Decision Trees, Boosting and MetaCost (abstract, gzipped ps)

Feng Yu Lin & Sally McClean: The Prediction of Financial Distress using a Cost Sensitive approach and Prior Probabilities (gzipped ps)

Terran Lane: Extensions of ROC Analysis to Multi-Class Domains (abstract, gzipped ps)

Chris Drummond & Robert Holte: Explicitly Representing Expected Cost: An Alternative to ROC Representation (gzipped ps)

Dragos Margineantu: On Class-Probability Estimates and Cost-Sensitive Evaluation of Classifiers (gzipped ps)

Edwin P.D.Pednault, Barry Rosen & Chidanand Apte: The Importance of Estimation Errors in Cost-Sensitive Learning (gzipped ps)