Elements of information theory
Elements of information theory
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Enhanced word clustering for hierarchical text classification
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Tree Induction for Probability-Based Ranking
Machine Learning
Active Sampling for Class Probability Estimation and Ranking
Machine Learning
Diverse ensembles for active learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
MOB-ESP and other improvements in probability estimation
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Constructing diverse classifier ensembles using artificial training examples
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Active learning for class probability estimation and ranking
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce
Proceedings of the ninth international conference on Electronic commerce
Guest editorial: special issue on utility-based data mining
Data Mining and Knowledge Discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multi-class ensemble-based active learning
ECML'06 Proceedings of the 17th European conference on Machine Learning
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Active selection of good training examples is an important approach to reducing data-collection costs in machine learning; however, most existing methods focus on maximizing classification accuracy. In many applications, such as those with unequal misclassification costs, producing good class probability estimates (CPEs) is more important than optimizing classification accuracy. We introduce novel approaches to active learning based on the algorithms Bootstrap-LV and ActiveDecorate, by using Jensen-Shannon divergence (a similarity measure for probability distributions) to improve sample selection for optimizing CPEs. Comprehensive experimental results demonstrate the benefits of our approaches.