# Improving the performance of bagging ensembles for data streams through mini-batching

arXiv: https://arxiv.org/abs/2112.09834 · HTML: https://mlsanity.com/paper/2112.09834/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 50 references that are in the map.
- First posted on arXiv 18 Dec 2021.

## Builds on (most cited first, 5 of 5)

- [BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning](https://mlsanity.com/paper/2002.06715/) (2020)
- [Evolving Ensemble Fuzzy Classifier](https://mlsanity.com/paper/1705.06460/) (2017)
- [AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles](https://mlsanity.com/paper/1905.00080/) (2019)
- [FastForest: Increasing Random Forest Processing Speed While Maintaining Accuracy](https://mlsanity.com/paper/2004.02423/) (2020)
- [On-line Application Autotuning Exploiting Ensemble Models](https://mlsanity.com/paper/1901.06228/) (2019)

## Built on by (most cited first, 1 of 1)

- [Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing](https://mlsanity.com/paper/2201.06205/) (2022)

## Abstract (arXiv)

Often, machine learning applications have to cope with dynamic environments where data are collected in the form of continuous data streams with potentially infinite length and transient behavior. Compared to traditional (batch) data mining, stream processing algorithms have additional requirements regarding computational resources and adaptability to data evolution. They must process instances incrementally because the data's continuous flow prohibits storing data for multiple passes. Ensemble learning achieved remarkable predictive performance in this scenario. Implemented as a set of (several) individual classifiers, ensembles are naturally amendable for task parallelism. However, the incremental learning and dynamic data structures used to capture the concept drift increase the cache misses and hinder the benefit of parallelism. This paper proposes a mini-batching strategy that can improve memory access locality and performance of several ensemble algorithms for stream mining in multi-core environments. With the aid of a formal framework, we demonstrate that mini-batching can significantly decrease the reuse distance (and the number of cache misses). Experiments on six different state-of-the-art ensemble algorithms applying four benchmark datasets with varied characteristics show speedups of up to 5X on 8-core processors. These benefits come at the expense of a small reduction in predictive performance.