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# A Deep Neural Network Conformal Predictor for Multi-label Text Classification

*Proceedings of the Eighth Symposium on Conformal and Probabilistic Prediction and Applications*, PMLR 105:228-245, 2019.

#### Abstract

We investigate the use of inductive conformal prediction (ICP)
for the task of multi-label text classification
and present preliminary experimental results for a subset of the original Reuters-21578 data-set.
Our underlying classification model is a deep neural network configuration
which consists of a trainable embedding layer, a convolutional layer and two dense feed-forward layers,
arranged sequentially,
with sigmoid outputs representing the individual unique labels of the selected subset.
Following the power-set approach,
we assign nonconformity scores to label-sets
from which the corresponding p-values and prediction-sets are determined
and we experiment with a number of different versions of a nonconformity measure.
Our results indicate a good performance for the underlying model
which is carried on to the ICP without any significant accuracy loss
and with the added benefits of prediction-specific confidence information.
Prediction-sets are tight enough to be practically useful
even though the multi-label subset contains tens of thousands of possible label combinations
and empirical error-rates confirm that our outputs are well-calibrated.