Well-calibrated Confidence Measures for Multi-label Text Classification
  with a Large Number of Labels
Lysimachos Maltoudoglou Andreas Paisios Ladislav Lenc Jiří Martínek Pavel Král Harris Papadopoulos
We extend our previous work on Inductive Conformal Prediction (ICP) for
multi-label text classification and present a novel approach for addressing the
computational inefficiency of the Label Powerset (LP) ICP, arrising when
dealing with a high number of unique labels. We present experimental results
using the original and the proposed efficient LP-ICP on two English and one
Czech language data-sets. Specifically, we apply the LP-ICP on three deep
Artificial Neural Network (ANN) classifiers of two types: one based on
contextualised (bert) and two on non-contextualised (word2vec) word-embeddings.
In the LP-ICP setting we assign nonconformity scores to label-sets from which
the corresponding p-values and prediction-sets are determined. Our approach
deals with the increased computational burden of LP by eliminating from
consideration a significant number of label-sets that will surely have p-values
below the specified significance level. This reduces dramatically the
computational complexity of the approach while fully respecting the standard CP
guarantees. Our experimental results show that the contextualised-based
classifier surpasses the non-contextualised-based ones and obtains
state-of-the-art performance for all data-sets examined. The good performance
of the underlying classifiers is carried on to their ICP counterparts without
any significant accuracy loss, but with the added benefits of ICP, i.e. the
confidence information encapsulated in the prediction sets. We experimentally
demonstrate that the resulting prediction sets can be tight enough to be
practically useful even though the set of all possible label-sets contains more
than $1e+16$ combinations. Additionally, the empirical error rates of the
obtained prediction-sets confirm that our outputs are well-calibrated.