Interval Insensitive Loss for Ordinal Classification
Kostiantyn Antoniuk, Vojtěch Franc, Václav Hlaváč · Asian Conference on Machine Learning · 2014
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 ordinal classifiers learned from partially annotated examples can achieve accuracy close to the accuracy of classifiers learned from completely annotated examples.