Block-wise Partitioning for Extreme Multi-label Classification

TL;DR

This paper introduces a block-wise partitioning method for extreme multi-label classification that significantly reduces prediction time while maintaining high accuracy by clustering instances and labels.

Contribution

The paper proposes a novel block-wise partitioning approach that improves computational efficiency in extreme multi-label classification without sacrificing accuracy.

Findings

Significantly reduces prediction time in experiments.

Maintains comparable prediction accuracy to existing methods.

Effective clustering approach for large label sets.

Abstract

Extreme multi-label classification aims to learn a classifier that annotates an instance with a relevant subset of labels from an extremely large label set. Many existing solutions embed the label matrix to a low-dimensional linear subspace, or examine the relevance of a test instance to every label via a linear scan. In practice, however, those approaches can be computationally exorbitant. To alleviate this drawback, we propose a Block-wise Partitioning (BP) pretreatment that divides all instances into disjoint clusters, to each of which the most frequently tagged label subset is attached. One multi-label classifier is trained on one pair of instance and label clusters, and the label set of a test instance is predicted by first delivering it to the most appropriate instance cluster. Experiments on benchmark multi-label data sets reveal that BP pretreatment significantly reduces prediction time, and retains almost the same level of prediction accuracy.