Qingfeng Liu, Yang Feng
arXiv 6 May 2021 · Statistics — Machine Learning
arXiv:2105.02569 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A (1984) Classification and regression trees | 0.644 | 2 | 2 | 100% |
| 2 | Friedman, J.H (2001) Greedy function approximation: A gradient boosting machine | 0.644 | 2 | 2 | 100% |
| 3 | van der Laan, M.J., Polley, E.C., Hubbard, A.E (2007) Super learner | 0.644 | 2 | 2 | 100% |
| 4 | Breiman, L (1996) Bagging predictors | 0.511 | 2 | 1 | 100% |
| 5 | Qi, Y., Liu, B., Wang, Y., Pan, G (2019) Dynamic ensemble modeling approach to nonstationary neural decoding in brain-computer interfaces | 0.405 | 1 | 1 | 100% |
| 6 | Lu, X., Van Roy, B (2017) Ensemble sampling | 0.405 | 1 | 1 | 100% |
| 7 | Olson, R.S., La Cava, W., Orzechowski, P., Urbanowicz, R.J., Moore,… (2017) Pmlb: a large benchmark suite for machine learning evaluation and comparison | 0.405 | 1 | 1 | 100% |
| 8 | Schapire, R.E., Freund, Y., Bartlett, P., Lee, W.S (1998) Boosting the margin: A new explanation for the effectiveness of voting methods | 0.405 | 1 | 1 | 100% |
| 9 | Schapire, R.E (1990) The strength of weak learnability | 0.405 | 1 | 1 | 100% |
| 10 | Chollet, F (2017) Deep learning with Python | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 23 scored citations.