4
$\begingroup$

I'm curious as to whether research been done into random forests that combine unsupervised with supervised learning in a way allowing a single algorithm to find patterns in, and work with, multiple different data sets. I have googled every possible way to find research on this, and have come up empty. Can anyone point me in the right direction?

$\endgroup$
4
$\begingroup$

Semi-Supervised Learning

The combination of unsupervised learning and supervised learning is referred to as semi-supervised learning, which is the concept that I believe you are searching for.

Label propagation is often cited when outlining the heuristics of semi-supervised learning. The essence is to employ clustering, but to use a tiny set of known cases in order to derive (or propogate) the labels of the clusters. Hence one is able to use a small set of labeled cases to classify a much larger set of unsupervised data.

Here are some references:

Hope this helps!

| improve this answer | |
$\endgroup$
  • 1
    $\begingroup$ This is genius! Thank you so much. I am new to data science (as a university student) and I had no idea where to start to learn for myself. Those links are perfect. $\endgroup$ – Jessiah Burgess Dec 31 '15 at 0:25

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.