# A Novel Online Stacked Ensemble for Multi-Label Stream Classification

arXiv: https://arxiv.org/abs/1809.09994 · HTML: https://mlsanity.com/paper/1809.09994/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2018.

## Built on by (most cited first, 7 of 7)

- [Balancing Efficiency vs. Effectiveness and Providing Missing Label Robustness in Multi-Label Stream Classification](https://mlsanity.com/paper/2310.00665/) (2023)
- [A Broad Ensemble Learning System for Drifting Stream Classification](https://mlsanity.com/paper/2110.03540/) (2021)
- [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)
- [Implicit Concept Drift Detection for Multi-label Data Streams](https://mlsanity.com/paper/2202.00070/) (2022)
- [Online Multi-Label Classification under Noisy and Changing Label Distribution](https://mlsanity.com/paper/2410.02394/) (2024)
- [Multi-Label Transfer Learning in Non-Stationary Data Streams](https://mlsanity.com/paper/2509.08181/) (2025)

## Abstract (arXiv)

As data streams become more prevalent, the necessity for online algorithms that mine this transient and dynamic data becomes clearer. Multi-label data stream classification is a supervised learning problem where each instance in the data stream is classified into one or more pre-defined sets of labels. Many methods have been proposed to tackle this problem, including but not limited to ensemble-based methods. Some of these ensemble-based methods are specifically designed to work with certain multi-label base classifiers; some others employ online bagging schemes to build their ensembles. In this study, we introduce a novel online and dynamically-weighted stacked ensemble for multi-label classification, called GOOWE-ML, that utilizes spatial modeling to assign optimal weights to its component classifiers. Our model can be used with any existing incremental multi-label classification algorithm as its base classifier. We conduct experiments with 4 GOOWE-ML-based multi-label ensembles and 7 baseline models on 7 real-world datasets from diverse areas of interest. Our experiments show that GOOWE-ML ensembles yield consistently better results in terms of predictive performance in almost all of the datasets, with respect to the other prominent ensemble models.