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Cascading Randomized Weighted Majority: A New Online Ensemble Learning Algorithm
Machine Learning · Submitted 3 Mar 2014
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We propose a cascading version of RWM to achieve not only better experimental results but also a better error bound for sufficiently large datasets.
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The abstract, sentence by sentence
With the increasing volume of data in the world, the best approach for learning from this data is to exploit an online learning algorithm. Online ensemble methods are online algorithms which take advantage of an ensemble of classifiers to predict labels of data. Prediction with expert advice is a well-studied problem in the online ensemble learning literature. The Weighted Majority algorithm and the randomized weighted majority (RWM) are the most well-known solutions to this problem, aiming to converge to the best expert. Since among some expert, the best one does not necessarily have the minimum error in all regions of data space, defining specific regions and converging to the best expert in each of these regions will lead to a better result. In this paper, we aim to resolve this defect of RWM algorithms by proposing a novel online ensemble algorithm to the problem of prediction with expert advice. We propose a cascading version of RWM to achieve not only better experimental results but also a better error bound for sufficiently large datasets.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | With the increasing volume of data in the world, the best approach for learning from this data is to exploit an online learning algorithm. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | Online ensemble methods are online algorithms which take advantage of an ensemble of classifiers to predict labels of data. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s2 | Prediction with expert advice is a well-studied problem in the online ensemble learning literature. | background 1.00 | 0.03 | 0.02 | asserted 1.00 |
| s3 | The Weighted Majority algorithm and the randomized weighted majority (RWM) are the most well-known solutions to this problem, aiming to converge to the best expert. | background 1.00 | 0.03 | 0.03 | asserted 1.00 |
| s4 | Since among some expert, the best one does not necessarily have the minimum error in all regions of data space, defining specific regions and converging to the best expert in each of these regions will lead to a better result. | gap 0.87 | 0.06 | 0.07 | asserted 0.63 |
| s5 | In this paper, we aim to resolve this defect of RWM algorithms by proposing a novel online ensemble algorithm to the problem of prediction with expert advice. | contribution 0.85 | 0.66 | 0.12 | asserted 0.86 |
| s6 | We propose a cascading version of RWM to achieve not only better experimental results but also a better error bound for sufficiently large datasets. | contribution 0.95 | 0.89 | 0.72 | asserted 0.85 |
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The paper
Original abstract
With the increasing volume of data in the world, the best approach for learning from this data is to exploit an online learning algorithm. Online ensemble methods are online algorithms which take advantage of an ensemble of classifiers to predict labels of data. Prediction with expert advice is a well-studied problem in the online ensemble learning literature. The Weighted Majority algorithm and the randomized weighted majority (RWM) are the most well-known solutions to this problem, aiming to converge to the best expert. Since among some expert, the best one does not necessarily have the minimum error in all regions of data space, defining specific regions and converging to the best expert in each of these regions will lead to a better result. In this paper, we aim to resolve this defect of RWM algorithms by proposing a novel online ensemble algorithm to the problem of prediction with expert advice. We propose a cascading version of RWM to achieve not only better experimental results but also a better error bound for sufficiently large datasets.
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