I am trying to use patent texts to make a binary classification of a large number of patents as either 1. related to automation or 2. not related to automation.

I have manually classified a small non-random subset of patents and would like to use them to train a classifier. I formed this subset by looking at all firms that had patented an automation technology in a small window of time. Non-automation patents in the subset are thus going to be quite different than non-automation patents in the whole data because they are filed by firms that do some work in automation.

My question has two parts:

  1. Is there a classification method that can be used with "biased" training data? (My hunch is no but never hurts to ask.)

  2. Alternatively, is there a classification method that will simply identify which patent texts are most like the ones I classified as automation patents?

Your Answer

 
discard

By clicking "Post Your Answer", you acknowledge that you have read our updated terms of service, privacy policy and cookie policy, and that your continued use of the website is subject to these policies.

Browse other questions tagged or ask your own question.