Conformal prediction is a recently developed framework of conﬁdent machine learning with guaranteed validity properties for prediction sets. In this work we study its usage in reversed version of the traditional machine learning problem: prediction of objects which can have a given label, instead of usual prediction of labels by objects. It is meant that the label reﬂect some desired property of the object. For this kind of task, the conformal prediction framework can provide a prediction set that is a set of objects that are likely to have the label. Based on this, we create an on-line protocol of experimental design. It includes a choice criterion based on conformal output, and elements of transfer learning in order to keep the validity properties in on-line regime.
|Title of host publication
|6th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2017)
|Number of pages
|Published - Jun 2017
- Conﬁdent classiﬁcation, conformal prediction, experimental design, transfer learning