1 research outputs found
Pointwise Binary Classification with Pairwise Confidence Comparisons
Ordinary (pointwise) binary classification aims to learn a binary classifier
from pointwise labeled data. However, such pointwise labels may not be directly
accessible due to privacy, confidentiality, or security considerations. In this
case, can we still learn an accurate binary classifier? This paper proposes a
novel setting, namely pairwise comparison (Pcomp) classification, where we are
given only pairs of unlabeled data that we know one is more likely to be
positive than the other, instead of pointwise labeled data. Pcomp
classification is useful for private or subjective classification tasks. To
solve this problem, we present a mathematical formulation for the generation
process of pairwise comparison data, based on which we exploit an unbiased risk
estimator (URE) to train a binary classifier by empirical risk minimization and
establish an estimation error bound. We first prove that a URE can be derived
and improve it using correction functions. Then, we start from the noisy-label
learning perspective to introduce a progressive URE and improve it by imposing
consistency regularization. Finally, experiments validate the effectiveness of
our proposed solutions for Pcomp classification