# Online hierarchical partitioning of the output space in extreme multi-label data stream

arXiv: https://arxiv.org/abs/2507.20894 · HTML: https://mlsanity.com/paper/2507.20894/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 47 references that are in the map.
- First posted on arXiv 28 Jul 2025.

## Builds on (most cited first, 10 of 10)

- [River: machine learning for streaming data in Python](https://mlsanity.com/paper/2012.04740/) (2020)
- [Extremely Fast Decision Tree](https://mlsanity.com/paper/1802.08780/) (2018)
- [AMF: Aggregated Mondrian Forests for Online Learning](https://mlsanity.com/paper/1906.10529/) (2019)
- [Stochastic Gradient Trees](https://mlsanity.com/paper/1901.07777/) (2019)
- [On-device edge learning for IoT data streams: a survey](https://mlsanity.com/paper/2502.17788/) (2025)
- [How is a data-driven approach better than random choice in label space division for multi-label classification?](https://mlsanity.com/paper/1606.02346/) (2016)
- [Balancing Efficiency vs. Effectiveness and Providing Missing Label Robustness in Multi-Label Stream Classification](https://mlsanity.com/paper/2310.00665/) (2023)
- [Hoeffding adaptive trees for multi-label classification on data streams](https://mlsanity.com/paper/2410.20242/) (2024)
- [Hierarchical Partitioning of the Output Space in Multi-label Data](https://mlsanity.com/paper/1612.06083/) (2016)
- [Online Multi-target regression trees with stacked leaf models](https://mlsanity.com/paper/1903.12483/) (2019)

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

- [Bridging Streaming Continual Learning via In-Context Large Tabular Models](https://mlsanity.com/paper/2512.11668/) (2025)
- [Explainable Anomaly Detection for Industrial IoT Data Streams](https://mlsanity.com/paper/2512.08885/) (2025)
- [Axle Sensor Fusion for Online Continual Wheel Fault Detection in Wayside Railway Monitoring](https://mlsanity.com/paper/2602.16101/) (2026)
- [Streaming Hierarchical Inference with Tabular Foundation Models](https://mlsanity.com/paper/2609.07956/) (2026)
- [Collaborative Streaming Anomaly Detection with Interactive Explanations and Ensemble Consensus](https://mlsanity.com/paper/2609.23883/) (2026)

## Abstract (arXiv)

Mining data streams with multi-label outputs poses significant challenges due to evolving distributions, high-dimensional label spaces, sparse label occurrences, and complex label dependencies. Moreover, concept drift affects not only input distributions but also label correlations and imbalance ratios over time, complicating model adaptation. To address these challenges, structured learners are categorized into local and global methods. Local methods break down the task into simpler components, while global methods adapt the algorithm to the full output space, potentially yielding better predictions by exploiting label correlations. This work introduces iHOMER (Incremental Hierarchy Of Multi-label Classifiers), an online multi-label learning framework that incrementally partitions the label space into disjoint, correlated clusters without relying on predefined hierarchies. iHOMER leverages online divisive-agglomerative clustering based on textitJaccard similarity and a global tree-based learner driven by a multivariate textitBernoulli process to guide instance partitioning. To address non-stationarity, it integrates drift detection mechanisms at both global and local levels, enabling dynamic restructuring of label partitions and subtrees. Experiments across 23 real-world datasets show iHOMER outperforms 5 state-of-the-art global baselines, such as MLHAT, MLHT of Pruned Sets and iSOUPT, by 23%, and 12 local baselines, such as binary relevance transformations of kNN, EFDT, ARF, and ADWIN bagging/boosting ensembles, by 32%, establishing its robustness for online multi-label classification.