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
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
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
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Magical thinking in data mining: lessons from CoIL challenge 2000
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Pruning Decision Trees with Misclassification Costs
ECML '98 Proceedings of the 10th European Conference on Machine Learning
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Methods for cost-sensitive learning
Methods for cost-sensitive learning
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Does cost-sensitive learning beat sampling for classifying rare classes?
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
Time period identification of events in text
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Minimax Regret Classifier for Imprecise Class Distributions
The Journal of Machine Learning Research
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
Multi-class cost-sensitive boosting with p-norm loss functions
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Cost-sensitive learning with conditional Markov networks
Data Mining and Knowledge Discovery
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
Risk-Sensitive Learning via Minimization of Empirical Conditional Value-at-Risk
IEICE - Transactions on Information and Systems
On multi-class cost-sensitive learning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Cost-sensitive learning based on Bregman divergences
Machine Learning
Anytime induction of low-cost, low-error classifiers: a sampling-based approach
Journal of Artificial Intelligence Research
An empirical study of the noise impact on cost-sensitive learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
CSNL: A cost-sensitive non-linear decision tree algorithm
ACM Transactions on Knowledge Discovery from Data (TKDD)
Expert Systems with Applications: An International Journal
A granular agent evolutionary algorithm for classification
Applied Soft Computing
Linguistic cost-sensitive learning of genetic fuzzy classifiers for imprecise data
International Journal of Approximate Reasoning
Fast data acquisition in cost-sensitive learning
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
Cost-Sensitive learning of SVM for ranking
ECML'06 Proceedings of the 17th European conference on Machine Learning
Parameter inference of cost-sensitive boosting algorithms
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Decision tree classifiers sensitive to heterogeneous costs
Journal of Systems and Software
A simple methodology for soft cost-sensitive classification
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
A survey of cost-sensitive decision tree induction algorithms
ACM Computing Surveys (CSUR)
Measurement and modeling of eye-mouse behavior in the presence of nonlinear page layouts
Proceedings of the 22nd international conference on World Wide Web
Multimedia Tools and Applications
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Cost-sensitive learning addresses the issue of classification in the presence of varying costs associated with different types of misclassification. In this paper, we present a method for solving multi-class cost-sensitive learning problems using any binary classification algorithm. This algorithm is derived using hree key ideas: 1) iterative weighting; 2) expanding data space; and 3) gradient boosting with stochastic ensembles. We establish some theoretical guarantees concerning the performance of this method. In particular, we show that a certain variant possesses the boosting property, given a form of weak learning assumption on the component binary classifier. We also empirically evaluate the performance of the proposed method using benchmark data sets and verify that our method generally achieves better results than representative methods for cost-sensitive learning, in terms of predictive performance (cost minimization) and, in many cases, computational efficiency.