Smarter AI—machine learning without negative data

phys.org | 11/26/2018 | Staff
hubbog (Posted by) Level 3
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A research team from the RIKEN Center for Advanced Intelligence Project (AIP) has successfully developed a new method for machine learning that allows an AI to make classifications without what is known as "negative data," a finding which could lead to wider application to a variety of classification tasks.

In terms of real-life programs, when a retailer is trying to predict who will make a purchase, it can easily find data on customers who purchased from them (positive data), but it is basically impossible to obtain data on customers who did not purchase from them (negative data), since they do not have access to their competitors' data. Another example is a common task for app developers: they need to predict which users will continue using the app (positive) or stop (negative). However, when a user unsubscribes, the developers lose the user's data because they have to completely delete data regarding that user in accordance with the privacy policy to protect personal information.

Author - Takashi - Ishida - RIKEN - AIP

According to lead author Takashi Ishida from RIKEN AIP, "Previous classification methods could not cope with the situation where negative data were not available, but we have made it possible for computers to learn with only positive data, as long as we have a confidence score for our positive data, constructed from information such as buying intention or the active rate of app users. Using our new method, we can let computers learn a classifier only from positive data equipped with confidence."

Ishida proposed, together with researcher...
(Excerpt) Read more at: phys.org
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