The Use of Background Knowledge in Decision Tree Induction
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Cost-sensitive pruning of decision trees
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Pruning Decision Trees with Misclassification Costs
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Comparative Study of Cost-Sensitive Boosting Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
PRICAI '96 Proceedings of the 4th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
Journal of Artificial Intelligence Research
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A Study on the Effect of Class Distribution Using Cost-Sensitive Learning
DS '02 Proceedings of the 5th International Conference on Discovery Science
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
Maximum profit mining and its application in software development
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Metadata and its impact on libraries: Book Reviews
Journal of the American Society for Information Science and Technology
Addressing diverse user preferences in SQL-query-result navigation
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
A weighted rough set based method developed for class imbalance learning
Information Sciences: an International Journal
Instance weighting versus threshold adjusting for cost-sensitive classification
Knowledge and Information Systems
Get another label? improving data quality and data mining using multiple, noisy labelers
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Evolutionary Induction of Decision Trees for Misclassification Cost Minimization
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
A comparative study on rough set based class imbalance learning
Knowledge-Based Systems
Compact Rule Learner on Weighted Fuzzy Approximation Spaces for Class Imbalanced and Hybrid Data
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Tuning Data Mining Methods for Cost-Sensitive Regression: A Study in Loan Charge-Off Forecasting
Journal of Management Information Systems
A Weighted Rough Set Approach for Cost-Sensitive Learning
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
On multi-class cost-sensitive learning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Margin calibration in SVM class-imbalanced learning
Neurocomputing
Cost-Sensitive Learning Vector Quantization for Financial Distress Prediction
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Weighted rough set learning: towards a subjective approach
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Exploring an improved decision tree based weights
ICNC'09 Proceedings of the 5th international conference on Natural computation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Weighted learning vector quantization to cost-sensitive learning
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
RAMOBoost: ranked minority oversampling in boosting
IEEE Transactions on Neural Networks
Customer Validation of Commercial Predictive Models
Proceedings of the 2010 conference on Data Mining for Business Applications
Cost-sensitive case-based reasoning using a genetic algorithm: Application to medical diagnosis
Artificial Intelligence in Medicine
An extended tuning method for cost-sensitive regression and forecasting
Decision Support Systems
Maximal-margin approach for cost-sensitive learning based on scaled convex hull
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
Dynamic classifier ensemble model for customer classification with imbalanced class distribution
Expert Systems with Applications: An International Journal
Evolutionary induction of cost-sensitive decision trees
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Expert Systems with Applications: An International Journal
Decision tree classifiers sensitive to heterogeneous costs
Journal of Systems and Software
Towards cost-sensitive learning for real-world applications
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Cost-sensitive classification with unconstrained influence diagrams
SOFSEM'12 Proceedings of the 38th international conference on Current Trends in Theory and Practice of Computer Science
Building decision trees for the multi-class imbalance problem
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Evaluating component solver contributions to portfolio-based algorithm selectors
SAT'12 Proceedings of the 15th international conference on Theory and Applications of Satisfiability Testing
A survey of cost-sensitive decision tree induction algorithms
ACM Computing Surveys (CSUR)
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Editorial: Parameter-free classification in multi-class imbalanced data sets
Data & Knowledge Engineering
Class imbalance and the curse of minority hubs
Knowledge-Based Systems
Algorithm portfolios based on cost-sensitive hierarchical clustering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Training and assessing classification rules with imbalanced data
Data Mining and Knowledge Discovery
Information Sciences: an International Journal
Assessment of data quality in accounting data with association rules
Expert Systems with Applications: An International Journal
Multimedia Tools and Applications
Repeated labeling using multiple noisy labelers
Data Mining and Knowledge Discovery
Influence of class distribution on cost-sensitive learning: A case study of bankruptcy analysis
Intelligent Data Analysis
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We introduce an instance-weighting method to induce cost-sensitive trees. It is a generalization of the standard tree induction process where only the initial instance weights determine the type of tree to be induced驴minimum error trees or minimum high cost error trees. We demonstrate that it can be easily adapted to an existing tree learning algorithm. Previous research provides insufficient evidence to support the idea that the greedy divide-and-conquer algorithm can effectively induce a truly cost-sensitive tree directly from the training data. We provide this empirical evidence in this paper. The algorithm incorporating the instance-weighting method is found to be better than the original algorithm in terms of total misclassification costs, the number of high cost errors, and tree size in two-class data sets. The instance-weighting method is simpler and more effective in implementation than a previous method based on altered priors.