Statistical analysis with missing data
Statistical analysis with missing data
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
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
On Active Learning for Data Acquisition
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Active Feature-Value Acquisition for Classifier Induction
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Economical active feature-value acquisition through Expected Utility estimation
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Budgeted learning of nailve-bayes classifiers
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce
Proceedings of the ninth international conference on Electronic commerce
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
Customer targeting models using actively-selected web content
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Estimating the utility value of individual credit card delinquents
Expert Systems with Applications: An International Journal
Bellwether analysis: Searching for cost-effective query-defined predictors in large databases
ACM Transactions on Knowledge Discovery from Data (TKDD)
Active Feature-Value Acquisition
Management Science
Efficiently learning the accuracy of labeling sources for selective sampling
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Active dual supervision: reducing the cost of annotating examples and features
HLT '09 Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing
Sample selection for statistical parsers: cognitively driven algorithms and evaluation measures
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Designing efficient cascaded classifiers: tradeoff between accuracy and cost
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A unified approach to active dual supervision for labeling features and examples
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Selecting actions for resource-bounded information extraction using reinforcement learning
Proceedings of the fifth ACM international conference on Web search and data mining
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Resource-Bounded information extraction: acquiring missing feature values on demand
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Besting the quiz master: crowdsourcing incremental classification games
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
New algorithms for budgeted learning
Machine Learning
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 classification tasks training data have missing feature values that can be acquired at a cost. For building accurate predictive models, acquiring all missing values is often prohibitively expensive or unnecessary, while acquiring a random subset of feature values may not be most effective. The goal of active feature-value acquisition is to incrementally select feature values that are most cost-effective for improving the model's accuracy. We present an approach that acquires feature values for inducing a classification model based on an estimation of the expected improvement in model accuracy per unit cost. Experimental results demonstrate that our approach consistently reduces the cost of producing a model of a desired accuracy compared to random feature acquisitions.