Dynamic Principal Projection for Cost-Sensitive Online Multi-Label Classification

TL;DR

This paper introduces CS-DPP, a novel online multi-label classification algorithm that simultaneously addresses label space reduction, cost-sensitivity, and online updating, demonstrating superior performance through theoretical guarantees and experiments.

Contribution

The paper presents CS-DPP, the first algorithm to jointly handle online updating, label space reduction, and cost-sensitivity in multi-label classification.

Findings

CS-DPP outperforms existing algorithms on multiple metrics.

Theoretical guarantees support the algorithm's effectiveness.

Experimental results highlight the importance of addressing all three issues together.

Abstract

We study multi-label classification (MLC) with three important real-world issues: online updating, label space dimensional reduction (LSDR), and cost-sensitivity. Current MLC algorithms have not been designed to address these three issues simultaneously. In this paper, we propose a novel algorithm, cost-sensitive dynamic principal projection (CS-DPP) that resolves all three issues. The foundation of CS-DPP is an online LSDR framework derived from a leading LSDR algorithm. In particular, CS-DPP is equipped with an efficient online dimension reducer motivated by matrix stochastic gradient, and establishes its theoretical backbone when coupled with a carefully-designed online regression learner. In addition, CS-DPP embeds the cost information into label weights to achieve cost-sensitivity along with theoretical guarantees. Experimental results verify that CS-DPP achieves better practical performance than current MLC algorithms across different evaluation criteria, and demonstrate the importance of resolving the three issues simultaneously.