A Theoretical Analysis of Why Hybrid Ensembles Work
Author
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-01-31
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Inspired by the group decision making process, ensembles or combinations of classifiers have been found favorable in a wide variety of application domains.
Some researchers propose to use the mixture of two different types of classification algorithms to create a hybrid ensemble.
Why does such an ensemble work? The question remains.
Following the concept of diversity, which is one of the fundamental elements of the success of ensembles, we conduct a theoretical analysis of why hybrid ensembles work, connecting using different algorithms to accuracy gain.
We also conduct experiments on classification performance of hybrid ensembles of classifiers created by decision tree and naïve Bayes classification algorithms, each of which is a top data mining algorithm and often used to create non-hybrid ensembles.
Therefore, through this paper, we provide a complement to the theoretical foundation of creating and using hybrid ensembles.
American Psychological Association (APA)
Hsu, Kuo-Wei. 2017. A Theoretical Analysis of Why Hybrid Ensembles Work. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1139845
Modern Language Association (MLA)
Hsu, Kuo-Wei. A Theoretical Analysis of Why Hybrid Ensembles Work. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1139845
American Medical Association (AMA)
Hsu, Kuo-Wei. A Theoretical Analysis of Why Hybrid Ensembles Work. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1139845
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139845