Data-driven decision model based on local two-stage weighted ensemble learning

Xu, Chunjie; Chang, Wenjun; Liu, Weiyong

doi:10.1007/s10479-022-04599-2

Cited by 2 publications

(1 citation statement)

References 60 publications

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“…Xu et al [130] proposed a decision model based on an ensemble learning approach through the following two-stage scheme. This approach adopts the dynamic weighting of the base classifiers which can be learned from successful decisions history.…”

Section: Related Workmentioning

confidence: 99%

Ensemble learning based on relative accuracy approach and diversity teams

B. Rokaya,

D. Alsufiani

2024

Bulletin EEI

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Ensemble learning, which involves combining the opinions of multiple experts to arrive at a better result, has been used for centuries. In this work, a review of the major voting methods in ensemble learning is explored. This work will focus on a new method for combining the results of individual learners. The method depends on the relative accuracy and diversity of teams. Instead of trying to assign weight to each different trainer, the concept of diversity teams is presented. Each team will vote as one player; however, the individual accuracies of each learner still be implemented. The concept of relaxing parameters that deal with each team as one player is presented. Our experiments demonstrate that traditional ensemble voting methods outperform individual learners. There is a limit to the superiority of the ensemble learner that any ensemble learner cannot go beyond. The proposed voting method gives the same results as the traditional ensemble voting methods, however, a different diversity of the proposed method from the traditional voting method or for different values of the relaxing parameter can be achieved.

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Section: Related Workmentioning

confidence: 99%