Label filters for large scale multilabel classification

Abstract

When assigning labels to a test instance, most multilabel and multiclass classifiers systematically evaluate every single label to decide whether it is relevant or not. This linear scan over labels becomes prohibitive when the number of labels is very large. To alleviate this problem we propose a two step approach where computationally efficient label filters pre-select a small set of candidate labels before the base multiclass or multilabel classifier is applied. The label filters select candidate labels by projecting a test instance on a filtering line, and retaining only the labels that have training instances in the vicinity of this projection. The filter parameters are learned directly from data by solving a constraint optimization problem, and are independent of the base multilabel classifier. The proposed label filters can be used in conjunction with any multiclass or multilabel classifier that requires a linear scan over the labels, and speed up prediction by orders of magnitude without significant impact on performance.