Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
C4.5: programs for machine learning
C4.5: programs for machine learning
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluating cost-sensitive Unsolicited Bulk Email categorization
Proceedings of the 2002 ACM symposium on Applied computing
Computer-Aided Multivariate Analysis
Computer-Aided Multivariate Analysis
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Class Probability Estimation and Cost-Sensitive Classification Decisions
ECML '02 Proceedings of the 13th European Conference on Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Convex Hull Ensemble Machine for Regression and Classification
Knowledge and Information Systems
An evolutionary approach for automatically extracting intelligible classification rules
Knowledge and Information Systems
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data
Knowledge and Information Systems
Using discriminant analysis for multi-class classification: an experimental investigation
Knowledge and Information Systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Evaluating and Tuning Predictive Data Mining Models Using Receiver Operating Characteristic Curves
Journal of Management Information Systems
The class imbalance problem: A systematic study
Intelligent Data Analysis
Intelligent Data Analysis
Constrained Cascade Generalization of Decision Trees
IEEE Transactions on Knowledge and Data Engineering
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
An Efficient Algorithm for Generating Generalized Decision Forests
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Expert Systems with Applications: An International Journal
Commercial Internet filters: Perils and opportunities
Decision Support Systems
An extended tuning method for cost-sensitive regression and forecasting
Decision Support Systems
Tuning expert systems for cost-sensitive decisions
Advances in Artificial Intelligence
Two New Prediction-Driven Approaches to Discrete Choice Prediction
ACM Transactions on Management Information Systems (TMIS)
Influence of class distribution on cost-sensitive learning: A case study of bankruptcy analysis
Intelligent Data Analysis
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In real-world classification problems, different types of misclassification errors often have asymmetric costs, thus demanding cost-sensitive learning methods that attempt to minimize average misclassification cost rather than plain error rate. Instance weighting and post hoc threshold adjusting are two major approaches to cost-sensitive classifier learning. This paper compares the effects of these two approaches on several standard, off-the-shelf classification methods. The comparison indicates that the two approaches lead to similar results for some classification methods, such as Naïve Bayes, logistic regression, and backpropagation neural network, but very different results for other methods, such as decision tree, decision table, and decision rule learners. The findings from this research have important implications on the selection of the cost-sensitive classifier learning approach as well as on the interpretation of a recently published finding about the relative performance of Naïve Bayes and decision trees.