# Scalable Multi-Output Label Prediction: From Classifier Chains to Classifier Trellises

arXiv: https://arxiv.org/abs/1501.04870 · HTML: https://mlsanity.com/paper/1501.04870/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2015.

## Built on by (most cited first, 3 of 3)

- [A Confidence-based Acquisition Model for Self-supervised Active Learning and Label Correction](https://mlsanity.com/paper/2310.08944/) (2023)
- [mldr.resampling: Efficient Reference Implementations of Multilabel Resampling Algorithms](https://mlsanity.com/paper/2305.17152/) (2023)
- [Estimating Conditional Covariance between labels for Multilabel Data](https://mlsanity.com/paper/2508.18951/) (2025)

## Abstract (arXiv)

Multi-output inference tasks, such as multi-label classification, have become increasingly important in recent years. A popular method for multi-label classification is classifier chains, in which the predictions of individual classifiers are cascaded along a chain, thus taking into account inter-label dependencies and improving the overall performance. Several varieties of classifier chain methods have been introduced, and many of them perform very competitively across a wide range of benchmark datasets. However, scalability limitations become apparent on larger datasets when modeling a fully-cascaded chain. In particular, the methods' strategies for discovering and modeling a good chain structure constitutes a mayor computational bottleneck. In this paper, we present the classifier trellis (CT) method for scalable multi-label classification. We compare CT with several recently proposed classifier chain methods to show that it occupies an important niche: it is highly competitive on standard multi-label problems, yet it can also scale up to thousands or even tens of thousands of labels.