MATLAB code for performing active search (Garnett, et al., "Bayesian Optimal Active Search and Surveying," ICML 2012).
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Active Search Toolbox for MATLAB

This repository contains MATLAB code for performing active search, as described in the following paper:

Garnett, R., Krishnamurthy, Y., Xiong, X., Schneider, J., and Mann, R. Bayesian Optimal Active Search and Surveying. (2012). International Conference on Machine Learning (ICML 2012).

This code is designed for use with the Active Learning Toolbox for MATLAB.


All code here assumes that the positive/sought class is encoded by "1"; all other classes are treated as uninteresting.


The following files are provided.


  • search_expected_utility: a score function implementing the (one-step) active search expected utility, for use directly in argmax (for one-step lookahead) or with expected_utility_lookahead (for multiple-step lookahead).
  • search_utility: a utility function implementing the active search utility.


The following files enable pruning the search space as described in the above paper. The pruning method relies on a bound on the maximum probability of being positive after observing a given additional number of positive observations. An interface is specified in expected_search_utility_bound, and one implementation is provided, corresponding to knn_model in the Active Learning Toolbox. To use pruning with a different model, you should write a probability bound corresponding to this interface and pass it to active_search_bound_selector.

  • active_search_bound_selector: a selector implementing the pruning method; only potentially optimal unlabeled points are selected
  • expected_search_utility_bound: a generic implementation of the required bound on expected utility to enable pruning, used by active_search_bound_selector
  • knn_probability_bound: an implementation of the probability bound required by expected_search_utility_bound for knn_model in the Active Learning Toolbox