Title: Cost-sensitive budget adaptive label thresholding algorithms for large-scale online multi-label classification

Authors: Rui Ding; Tingting Zhai

Addresses: College of Information Engineering, Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou, 225127, China ' College of Information Engineering, Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou, 225127, China

Abstract: Kernel-based methods have proven effective in addressing nonlinear online multi-label classification tasks. However, their scalability is hampered by the curse of kernelisation when handling large-scale tasks. Additionally, the class-imbalance of multi-label data can significantly impact their performance. To mitigate both challenges, we propose two cost-sensitive budget adaptive label thresholding algorithms. Firstly, we introduce a cost-sensitive strategy to assign varying costs to the misclassification of different labels, building upon the first-order adaptive label thresholding algorithm. Furthermore, we present two merging budget maintenance strategies: 1) a global strategy where all predictive models share one support vector pool and undergo simultaneous budgeting; 2) a separate strategy that utilises two independent support vector pools - one for the scoring models and the other for the thresholding model - with both models being budgeted independently. Experiments on seven datasets from various fields confirm the effectiveness and superiority of our proposed algorithms, particularly in large-scale online multi-label classification tasks.

Keywords: kernel-based methods; large-scale online multi-label classification; curse of kernelisation; class-imbalance; cost-sensitive; budget maintenance.

DOI: 10.1504/IJCSE.2024.141338

International Journal of Computational Science and Engineering, 2024 Vol.27 No.5, pp.597 - 606

Received: 13 Apr 2023
Accepted: 09 Sep 2023
Published online: 09 Sep 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article