Online Multi-Label Classification: A Label Compression Method

TL;DR

This paper introduces a fast linear label space reduction technique for online multi-label classification, improving efficiency and performance in handling large label sets with dependencies.

Contribution

It proposes a novel label compression method that reduces computational costs and enables online updates, addressing limitations of existing multi-label classifiers.

Findings

Significantly faster training times compared to traditional methods.

Maintains high prediction accuracy across various evaluation metrics.

Effective in handling large-scale, real-world multi-label data.

Abstract

Many modern applications deal with multi-label data, such as functional categorizations of genes, image labeling and text categorization. Classification of such data with a large number of labels and latent dependencies among them is a challenging task, and it becomes even more challenging when the data is received online and in chunks. Many of the current multi-label classification methods require a lot of time and memory, which make them infeasible for practical real-world applications. In this paper, we propose a fast linear label space dimension reduction method that transforms the labels into a reduced encoded space and trains models on the obtained pseudo labels. Additionally, it provides an analytical method to update the decoding matrix which maps the labels into the original space and is used during the test phase. Experimental results show the effectiveness of this approach in terms of running times and the prediction performance over different measures.