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 upon support-vector machines (SVMs) and exploit the structure of the SVM to determine which data points to label. Such methods usually calculate the margin, , of each unlabeled datum in and treat as an -dimensional distance from that datum to the separating hyperplane. Minimum Marginal Hyperplane methods assume that the data with the smallest are those that the SVM is most uncertain about and therefore should be placed in to be labeled. Other similar methods, such as Maximum Marginal Hyperplane, choose data with the largest . Tradeoff methods choose a mix of the smallest and largest s.See also
* List of datasets for machine learning researchNotes
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