Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Learning cost-sensitive active classifiers
Artificial Intelligence
Class Probability Estimation and Cost-Sensitive Classification Decisions
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Tree Induction for Probability-Based Ranking
Machine Learning
Active learning for class probability estimation and ranking
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Partial example acquisition in cost-sensitive learning
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Active learning with direct query construction
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Data Mining and Knowledge Discovery
A self-training approach to cost sensitive uncertainty sampling
Machine Learning
Anytime induction of low-cost, low-error classifiers: a sampling-based approach
Journal of Artificial Intelligence Research
Asking generalized queries to ambiguous oracle
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Interactive learning for efficiently detecting errors in insurance claims
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Asking generalized queries with minimum cost
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Metric anomaly detection via asymmetric risk minimization
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Repeated labeling using multiple noisy labelers
Data Mining and Knowledge Discovery
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For many classification tasks a large number of instances available for training are unlabeled and the cost associated with the labeling process varies over the input space. Meanwhile, virtually all these problems require classifiers that minimize a nonuniform loss function associated with the classification decisions (rather than the accuracy or number of errors). For example, to train pattern classification models for a network intrusion detection task, experts need to analyze network events and assign them labels. This can be a very costly procedure if the instances to be labeled are selected at random. In the meantime, the loss associated with mislabeling an intrusion is much higher than the loss associated with the opposite error (i.e., labeling a legal event as being an intrusion). As a result, to address these types of tasks, practitioners need tools that minimize the total cost computed as a sum of the cost of labeling and the loss associated with the decisions. This paper describes an approach for addressing this problem.