# Balancing Efficiency vs. Effectiveness and Providing Missing Label Robustness in Multi-Label Stream Classification

arXiv: https://arxiv.org/abs/2310.00665 · HTML: https://mlsanity.com/paper/2310.00665/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 64 references that are in the map.
- First posted on arXiv 1 Oct 2023.

## Builds on (most cited first, 8 of 8)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification](https://mlsanity.com/paper/1811.01727/) (2018)
- [The Emerging Trends of Multi-Label Learning](https://mlsanity.com/paper/2011.11197/) (2020)
- [Structured Semantic Transfer for Multi-Label Recognition with Partial Labels](https://mlsanity.com/paper/2112.10941/) (2021)
- [Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations](https://mlsanity.com/paper/1608.01441/) (2016)
- [Multi-label Learning with Missing Labels using Mixed Dependency Graphs](https://mlsanity.com/paper/1804.00117/) (2018)
- [A Novel Online Stacked Ensemble for Multi-Label Stream Classification](https://mlsanity.com/paper/1809.09994/) (2018)
- [A Broad Ensemble Learning System for Drifting Stream Classification](https://mlsanity.com/paper/2110.03540/) (2021)

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

- [Online hierarchical partitioning of the output space in extreme multi-label data stream](https://mlsanity.com/paper/2507.20894/) (2025)
- [Hoeffding adaptive trees for multi-label classification on data streams](https://mlsanity.com/paper/2410.20242/) (2024)
- [A Systematic Literature Review on Multi-label Data Stream Classification](https://mlsanity.com/paper/2508.17455/) (2025)
- [Jump-teaching: Combating Sample Selection Bias via Temporal Disagreement](https://mlsanity.com/paper/2405.17137/) (2024)
- [Online Multi-Label Classification under Noisy and Changing Label Distribution](https://mlsanity.com/paper/2410.02394/) (2024)

## Abstract (arXiv)

Available works addressing multi-label classification in a data stream environment focus on proposing accurate models; however, these models often exhibit inefficiency and cannot balance effectiveness and efficiency. In this work, we propose a neural network-based approach that tackles this issue and is suitable for high-dimensional multi-label classification. Our model uses a selective concept drift adaptation mechanism that makes it suitable for a non-stationary environment. Additionally, we adapt our model to an environment with missing labels using a simple yet effective imputation strategy and demonstrate that it outperforms a vast majority of the state-of-the-art supervised models. To achieve our purposes, we introduce a weighted binary relevance-based approach named ML-BELS using the Broad Ensemble Learning System (BELS) as its base classifier. Instead of a chain of stacked classifiers, our model employs independent weighted ensembles, with the weights generated by the predictions of a BELS classifier. We show that using the weighting strategy on datasets with low label cardinality negatively impacts the accuracy of the model; with this in mind, we use the label cardinality as a trigger for applying the weights. We present an extensive assessment of our model using 11 state-of-the-art baselines, five synthetics, and 13 real-world datasets, all with different characteristics. Our results demonstrate that the proposed approach ML-BELS is successful in balancing effectiveness and efficiency, and is robust to missing labels and concept drift.