A Cross-Conformal Predictor for Multi-label Classification
- URL: http://arxiv.org/abs/2211.16238v1
- Date: Tue, 29 Nov 2022 14:21:49 GMT
- Title: A Cross-Conformal Predictor for Multi-label Classification
- Authors: Harris Papadopoulos
- Abstract summary: In multi-label learning each instance is associated with multiple classes simultaneously.
This work examines the application of a recently developed framework called Conformal Prediction to the multi-label learning setting.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Unlike the typical classification setting where each instance is associated
with a single class, in multi-label learning each instance is associated with
multiple classes simultaneously. Therefore the learning task in this setting is
to predict the subset of classes to which each instance belongs. This work
examines the application of a recently developed framework called Conformal
Prediction (CP) to the multi-label learning setting. CP complements the
predictions of machine learning algorithms with reliable measures of
confidence. As a result the proposed approach instead of just predicting the
most likely subset of classes for a new unseen instance, also indicates the
likelihood of each predicted subset being correct. This additional information
is especially valuable in the multi-label setting where the overall uncertainty
is extremely high.
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