Margin-Based Pareto Ensemble Pruning: An Ensemble Pruning Algorithm That Learns to Search Optimized Ensembles
Joint Authors
Hu, Ruihan
Zhou, Songbin
Liu, Yisen
Tang, Zhiri
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-06-03
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
The ensemble pruning system is an effective machine learning framework that combines several learners as experts to classify a test set.
Generally, ensemble pruning systems aim to define a region of competence based on the validation set to select the most competent ensembles from the ensemble pool with respect to the test set.
However, the size of the ensemble pool is usually fixed, and the performance of an ensemble pool heavily depends on the definition of the region of competence.
In this paper, a dynamic pruning framework called margin-based Pareto ensemble pruning is proposed for ensemble pruning systems.
The framework explores the optimized ensemble pool size during the overproduction stage and finetunes the experts during the pruning stage.
The Pareto optimization algorithm is used to explore the size of the overproduction ensemble pool that can result in better performance.
Considering the information entropy of the learners in the indecision region, the marginal criterion for each learner in the ensemble pool is calculated using margin criterion pruning, which prunes the experts with respect to the test set.
The effectiveness of the proposed method for classification tasks is assessed using datasets.
The results show that margin-based Pareto ensemble pruning can achieve smaller ensemble sizes and better classification performance in most datasets when compared with state-of-the-art models.
American Psychological Association (APA)
Hu, Ruihan& Zhou, Songbin& Liu, Yisen& Tang, Zhiri. 2019. Margin-Based Pareto Ensemble Pruning: An Ensemble Pruning Algorithm That Learns to Search Optimized Ensembles. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1129576
Modern Language Association (MLA)
Hu, Ruihan…[et al.]. Margin-Based Pareto Ensemble Pruning: An Ensemble Pruning Algorithm That Learns to Search Optimized Ensembles. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1129576
American Medical Association (AMA)
Hu, Ruihan& Zhou, Songbin& Liu, Yisen& Tang, Zhiri. Margin-Based Pareto Ensemble Pruning: An Ensemble Pruning Algorithm That Learns to Search Optimized Ensembles. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1129576
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129576