COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Machine Learning
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Active learning using adaptive resampling
Proceedings of the sixth 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
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Machine Learning
Machine Learning
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth 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
Tree induction vs. logistic regression: a learning-curve analysis
The Journal of Machine Learning Research
Active learning with statistical models
Journal of Artificial Intelligence Research
Using asymmetric distributions to improve text classifier probability estimates
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Noisy information value in utility-based decision making
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Developing adaptive auction mechanisms
ACM SIGecom Exchanges
Feature value acquisition in testing: a sequential batch test algorithm
ICML '06 Proceedings of the 23rd international conference on Machine learning
Adaptive mechanism design: a metalearning approach
ICEC '06 Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet
Online Random Shuffling of Large Database Tables
IEEE Transactions on Knowledge and Data Engineering
Active EM to reduce noise in activity recognition
Proceedings of the 12th international conference on Intelligent user interfaces
Entropy-Driven online active learning for interactive calendar management
Proceedings of the 12th international conference on Intelligent user interfaces
Active exploration for learning rankings from clickthrough data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Partial example acquisition in cost-sensitive learning
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Selectively acquiring ratings for product recommendation
Proceedings of the ninth international conference on Electronic commerce
Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce
Proceedings of the ninth international conference on Electronic commerce
Active learning and logarithmic opinion pools for hpsg parse selection
Natural Language Engineering
A bayesian logistic regression model for active relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
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
Guest editorial: special issue on utility-based data mining
Data Mining and Knowledge Discovery
Decision Tree Instability and Active Learning
ECML '07 Proceedings of the 18th European conference on Machine Learning
Active Feature-Value Acquisition
Management Science
Information Market-Based Decision Fusion
Management Science
Improving data mining utility with projective sampling
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the ACM SIGKDD Workshop on Human Computation
A self-training approach to cost sensitive uncertainty sampling
Machine Learning
A machine learning approach to sentiment analysis in multilingual Web texts
Information Retrieval
Software testing by active learning for commercial games
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Actively exploring creation of face space(s) for improved face recognition
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
Reflect and correct: A misclassification prediction approach to active inference
ACM Transactions on Knowledge Discovery from Data (TKDD)
Adaptive Auction Mechanism Design and the Incorporation of Prior Knowledge
INFORMS Journal on Computing
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
Fast active exploration for link-based preference learning using Gaussian processes
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Improving Tree augmented Naive Bayes for class probability estimation
Knowledge-Based Systems
Active learning for probability estimation using jensen-shannon divergence
ECML'05 Proceedings of the 16th European conference on Machine Learning
DCPE co-training for classification
Neurocomputing
Not so greedy: Randomly Selected Naive Bayes
Expert Systems with Applications: An International Journal
Active surveying: a probabilistic approach for identifying key opinion leaders
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Intelligently querying incomplete instances for improving classification performance
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Repeated labeling using multiple noisy labelers
Data Mining and Knowledge Discovery
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In many cost-sensitive environments class probability estimates are used by decision makers to evaluate the expected utility from a set of alternatives. Supervised learning can be used to build class probability estimates; however, it often is very costly to obtain training data with class labels. Active learning acquires data incrementally, at each phase identifying especially useful additional data for labeling, and can be used to economize on examples needed for learning. We outline the critical features of an active learner and present a sampling-based active learning method for estimating class probabilities and class-based rankings. BOOTSTRAP-LV identifies particularly informative new data for learning based on the variance in probability estimates, and uses weighted sampling to account for a potential example's informative value for the rest of the input space. We show empirically that the method reduces the number of data items that must be obtained and labeled, across a wide variety of domains. We investigate the contribution of the components of the algorithm and show that each provides valuable information to help identify informative examples. We also compare BOOTSTRAP-LV with UNCERTAINTY SAMPLING, an existing active learning method designed to maximize classification accuracy. The results show that BOOTSTRAP-LV uses fewer examples to exhibit a certain estimation accuracy and provide insights to the behavior of the algorithms. Finally, we experiment with another new active sampling algorithm drawing from both UNCERTAINTY SAMPLING and BOOTSTRAP-LV and show that it is significantly more competitive with BOOTSTRAP-LV compared to UNCERTAINTY SAMPLING. The analysis suggests more general implications for improving existing active sampling algorithms for classification.