Interval Insensitive Loss for Ordinal Classification

Abstract

We address a problem of learning ordinal classifier from partially annotated examples. We introduce an interval-insensitive loss function to measure discrepancy between predictions of an ordinal classifier and a partial annotation provided in the form of intervals of admissible labels. The proposed interval-insensitive loss is an instance of loss functions previously used for learning of different classification models from partially annotated examples. We propose several convex surrogates of the interval-insensitive loss which can be efficiently optimized by existing solvers. Experiments on standard benchmarks and a real-life application show that learning ordinal classifiers from partially annotated examples is competitive to the so-far used methods learning from the complete annotation.