insufficient labeled data

Wednesday, September 14, 2016 - 10:00am - 10:50am
Vipin Kumar (University of Minnesota, Twin Cities)
Many real-world problems involve learning predictive models for rare classes in situations where there are no gold standard labels for training samples but imperfect labels are available for all instances. We present RAre class Prediction in absence of True labels (RAPT), a three step predictive modeling framework for classifying rare class in such problem settings. The first step of the RAPT framework learns a classifier that optimizes both precision and recall by only using imperfectly labeled training samples.
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