Definitions
Let be the total set of all data under consideration. For example, in a protein engineering problem, would include all proteins that are known to have a certain interesting activity and all additional proteins that one might want to test for that activity. During each iteration, , is broken up into three subsets #: Data points where the label is known. #: Data points where the label is unknown. #: A subset of that is chosen to be labeled. Most of the current research in active learning involves the best method to choose the data points for .Scenarios
*Membership Query Synthesis: This is where the learner generates its own instance from an underlying natural distribution. For example, if the dataset are pictures of humans and animals, the learner could send a clipped image of a leg to the teacher and query if this appendage belongs to an animal or human. This is particularly useful if the dataset is small. *Pool-Based Sampling: In this scenario, instances are drawn from the entire data pool and assigned a confidence score, a measurement of how well the learner “understands” the data. The system then selects the instances for which it is the least confident and queries the teacher for the labels. *Stream-Based Selective Sampling: Here, each unlabeled data point is examined one at a time with the machine evaluating the informativeness of each item against its query parameters. The learner decides for itself whether to assign a label or query the teacher for each datapoint.Query strategies
Algorithms for determining which data points should be labeled can be organized into a number of different categories, based upon their purpose: *Balance exploration and exploitation: the choice of examples to label is seen as a dilemma between the exploration and the exploitation over the data space representation. This strategy manages this compromise by modelling the active learning problem as a contextual bandit problem. For example, Bouneffouf et al. propose a sequential algorithm named Active Thompson Sampling (ATS), which, in each round, assigns a sampling distribution on the pool, samples one point from this distribution, and queries the oracle for this sample point label. *Expected model change: label those points that would most change the current model. *Expected error reduction: label those points that would most reduce the model's generalization error. *Exponentiated Gradient Exploration for Active Learning: In this paper, the author proposes a sequential algorithm named exponentiated gradient (EG)-active that can improve any active learning algorithm by an optimal random exploration. *Uncertainty sampling: label those points for which the current model is least certain as to what the correct output should be. *Query by committee: a variety of models are trained on the current labeled data, and vote on the output for unlabeled data; label those points for which the "committee" disagrees the most *Querying from diverse subspaces or partitions: When the underlying model is a forest of trees, the leaf nodes might represent (overlapping) partitions of the originalMinimum marginal hyperplane
Some active learning algorithms are built uponSee also
*Notes
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