Hello,
I work in a classification problem where each instance has several
attributes (e.g. the age of an individual). However, collecting instances
(either labeled or unlabeled) is very expensive, since it requires asking
domain experts to spend a significant amount of time to simply collect the
instance (labeling the instance once it has been collected is actually
relatively fast)
Given this, I want to explore an active learning strategy where rather than
starting with a set of labeled and unlabeled instances, I only have labeled
instances,* but *I can ask for additional labeled instances by specifying:
- Attributes or statistics of the attributes of the additional instances
(e.g. give me an instance with an age in the range [a,b]) on the new
instances
- The desired label of the additional instances (e.g. give me a new
instance with label x), or alternatively the *label *sampling
distribution that the experts should use get new instances.
With this, my questions are:
- Does this problem have a name? It looks like a specific case of Active
Learning, but I am not sure, since in Active Learning one starts with a set
of unlabeled instances, which is not my case.
- What types of approaches (from the most rudimentary to the more
sophisticated) can I employ to identify the most informative sampling
distribution from instance attributes or instance labels?
- Does *scikit-learn* provide any functionality geared towards the
specific challenges of this problem?
Thanks a lot,
Josh
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