# Making Classifier Chains Resilient to Class Imbalance

arXiv: https://arxiv.org/abs/1807.11393 · HTML: https://mlsanity.com/paper/1807.11393/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2018.

## Built on by (most cited first, 5 of 5)

- [Multi-Label Adaptive Batch Selection by Highlighting Hard and Imbalanced Samples](https://mlsanity.com/paper/2403.18192/) (2024)
- [AEMLO: AutoEncoder-Guided Multi-Label Oversampling](https://mlsanity.com/paper/2408.13078/) (2024)
- [Hybrid consistency and plausibility verification of product data according to FIC](https://mlsanity.com/paper/2102.02665/) (2021)
- [A Multivariate Bernoulli-Based Sampling Method for Multi-Label Data with Application to Meta-Research](https://mlsanity.com/paper/2512.08371/) (2025)
- [Addressing Imbalance in Multi-Label Data via Label-Specific Distance-based Oversampling](https://mlsanity.com/paper/2606.05927/) (2026)

## Abstract (arXiv)

Class imbalance is an intrinsic characteristic of multi-label data. Most of the labels in multi-label data sets are associated with a small number of training examples, much smaller compared to the size of the data set. Class imbalance poses a key challenge that plagues most multi-label learning methods. Ensemble of Classifier Chains (ECC), one of the most prominent multi-label learning methods, is no exception to this rule, as each of the binary models it builds is trained from all positive and negative examples of a label. To make ECC resilient to class imbalance, we first couple it with random undersampling. We then present two extensions of this basic approach, where we build a varying number of binary models per label and construct chains of different sizes, in order to improve the exploitation of majority examples with approximately the same computational budget. Experimental results on 16 multi-label datasets demonstrate the effectiveness of the proposed approaches in a variety of evaluation metrics.