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arXiv · 1811.01305

Block-wise Partitioning for Extreme Multi-label Classification

Yuefeng Liang·Cho-Jui Hsieh·Thomas C. M. Lee
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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.

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Yuefeng Liang, Cho-Jui Hsieh, Thomas C. M. Lee. 2018-11-04. Block-wise Partitioning for Extreme Multi-label Classification. https://arxiv.org/abs/1811.01305

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