This sounds a bit like *active class selection* rather than traditional
active learning. See this paper: http://dl.acm.org/citation.cfm?id=1421731.
You might also try the active learning mailing list:
[email protected].
Best,
-Byron
On Thu, Oct 3, 2013 at 10:01 AM, Josh Wasserstein <[email protected]>wrote:
> 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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--
byron
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