Active Sampling for Feature Selection
ICDM '03 Proceedings of the Third 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
Cost-Constrained Data Acquisition for Intelligent Data Preparation
IEEE Transactions on Knowledge and Data Engineering
An Expected Utility Approach to Active Feature-Value Acquisition
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Active sampling for detecting irrelevant features
ICML '06 Proceedings of the 23rd international conference on Machine learning
Maximizing classifier utility when training data is costly
ACM SIGKDD Explorations Newsletter
Customer targeting models using actively-selected web content
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Active Feature-Value Acquisition
Management Science
A decision support system for cost-effective diagnosis
Artificial Intelligence in Medicine
Active sampling for knowledge discovery from biomedical data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Reinforcement learning using a grid based function approximator
Biomimetic Neural Learning for Intelligent Robots
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
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Many applications are characterized by having naturallyincomplete data on customers - where data on only somefixed set of local variables is gathered. However, having amore complete picture can help build better models. Thenaïve solution to this problem - acquiring complete datafor all customers - is often impractical due to the costs ofdoing so. A possible alternative is to acquire completedata for "some" customers and to use this to improve themodels built. The data acquisition problem is determininghow many, and which, customers to acquire additionaldata from. In this paper we suggest using active learningbased approaches for the data acquisition problem. Inparticular, we present initial methods for data acquisitionand evaluate these methods experimentally on web usagedata and UCI datasets. Results show that the methodsperform well and indicate that active learning basedmethods for data acquisition can be a promising area fordata mining research.