# Machine Collaboration

arXiv: https://arxiv.org/abs/2105.02569 · HTML: https://mlsanity.com/paper/2105.02569/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 6 May 2021.

## Builds on (most cited first, 4 of 4)

- [PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison](https://mlsanity.com/paper/1703.00512/) (2017)
- [Ensemble Sampling](https://mlsanity.com/paper/1705.07347/) (2017)
- [Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces](https://mlsanity.com/paper/1911.00714/) (2019)
- [RaSE: Random Subspace Ensemble Classification](https://mlsanity.com/paper/2006.08855/) (2020)

## Abstract (arXiv)

We propose a new ensemble framework for supervised learning, called machine collaboration (MaC), using a collection of base machines for prediction tasks. Unlike bagging/stacking (a parallel & independent framework) and boosting (a sequential & top-down framework), MaC is a type of circular & interactive learning framework. The circular & interactive feature helps the base machines to transfer information circularly and update their structures and parameters accordingly. The theoretical result on the risk bound of the estimator from MaC reveals that the circular & interactive feature can help MaC reduce risk via a parsimonious ensemble. We conduct extensive experiments on MaC using both simulated data and 119 benchmark real datasets. The results demonstrate that in most cases, MaC performs significantly better than several other state-of-the-art methods, including classification and regression trees, neural networks, stacking, and boosting.