On the Influence of Hyperparameters in Tree-Based Linear Methods for Extreme Multi-Label Text Classification: Insights for Efficient and Effective Search
This study innovatively analyzes the hyperparameters of tree-based linear methods and suggests an efficient and effective guideline that leads to consistent improvements across datasets, thereby strengthening tree-based linear methods as a stronger XMTC baseline.
Extreme multi-label classification (XML) and hierarchical text classification (HTC) address closely related multi-label prediction problems, but have largely developed as separate research areas. XML focuses on very large label spaces and typically evaluates ranked label lists, while HTC assumes a human-curated label h...
Florian Hauss, Tom Speier, Nerijus Bertalis et al.· Proceedings of the 2026 ACM...· 0 citations
The findings confirm that combining hybrid ensemble learning with active learning offers an effective, lightweight, and cost-efficient alternative to purely transformer-based approaches, making it well-suited for real-world text classification tasks where labeled data are scarce or expensive.
DualMLC is proposed, a dual-branch framework that processes the same document through an autoregressive decoder-only language model and a bidirectional encoder, achieving state-of-the-art results on three widely used large-scale multi-label text classification benchmarks.
Hui Ye, Jing Zhang, Xiu-Long Yang et al.· 0 citations
This work proposes HyperLabel, an encoder-decoder framework that explicitly models label dependencies through hypergraph neural networks, and proposes a unified cross-modal learning approach where HGNN+ performs bidirectional message passing to integrate feature information with label structure, and a shared cross-atte...
Pei-Yu Zhang, Heng Ping, Nikos Kanakaris et al.· 0 citations
Data-Informed Centroid Splitting (DICS), a clustering-based framework that constructs a compact and informative set of candidate splits using data-driven priors, significantly reduces the split search space for classification tasks while preserving predictive performance.
This paper describes formally how MLC can be transformed into an order structure learning and prediction task, and then proceeds to study the problem of predicting Bayes-optimal order structures.
