Cost-Sensitive Reference Pair Encoding for Multi-Label Learning

TL;DR

This paper introduces CSRPE, a simple cost-sensitive encoding scheme for multi-label classification that improves performance and supports active learning, outperforming existing methods across various criteria.

Contribution

The paper proposes CSRPE, a novel, simpler cost-sensitive encoding scheme for multi-label learning that naturally incorporates cost information and enhances active learning capabilities.

Findings

CSRPE outperforms state-of-the-art algorithms in multiple MLC criteria.

The active learning algorithm based on CSRPE is superior to existing methods.

Extensive experiments validate the effectiveness of CSRPE and its active learning extension.

Abstract

Label space expansion for multi-label classification (MLC) is a methodology that encodes the original label vectors to higher dimensional codes before training and decodes the predicted codes back to the label vectors during testing. The methodology has been demonstrated to improve the performance of MLC algorithms when coupled with off-the-shelf error-correcting codes for encoding and decoding. Nevertheless, such a coding scheme can be complicated to implement, and cannot easily satisfy a common application need of cost-sensitive MLC---adapting to different evaluation criteria of interest. In this work, we show that a simpler coding scheme based on the concept of a reference pair of label vectors achieves cost-sensitivity more naturally. In particular, our proposed cost-sensitive reference pair encoding (CSRPE) algorithm contains cluster-based encoding, weight-based training and voting-based decoding steps, all utilizing the cost information. Furthermore, we leverage the cost information embedded in the code space of CSRPE to propose a novel active learning algorithm for cost-sensitive MLC. Extensive experimental results verify that CSRPE performs better than state-of-the-art algorithms across different MLC criteria. The results also demonstrate that the CSRPE-backed active learning algorithm is superior to existing algorithms for active MLC, and further justify the usefulness of CSRPE.