Proactive learning
Proactive learning[1] is a generalization of active learning designed to relax unrealistic assumptions and thereby reach practical applications.
Active learning seeks to select the most informative unlabeled instances and ask an omniscient oracle for their labels, so as to retrain the learning algorithm maximizing accuracy. However, the oracle is assumed to be infallible (never wrong), indefatigable (always answers), individual (only one oracle), and insensitive to costs (always free or always charges the same). Proactive learning relaxes all four of these assumptions, relying on a decision-theoretic approach to jointly select the optimal oracle and instance, by casting the problem as a utility optimization problem subject to a budget constraint.
References
- ^ Donmez, P., Carbonell, J.G.: Proactive Learning: Cost-Sensitive Active Learning with Multiple Imperfect Oracles, in Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM '08), Napa Valley 2008. http://www.cs.cmu.edu/~pinard/Papers/cikm0613-donmez.pdf