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Computational Intelligence and Neuroscience - Volume 2017 2017, Article ID 1930702, 12 pages - https:-doi.org-10.1155-2017-1930702

Research ArticleDepartment of Computer Science, National Chengchi University, No. 64, Sec. 2, Zhi Nan Rd., Wen Shan District, Taipei City 11605, Taiwan

Correspondence should be addressed to Kuo-Wei Hsu

Received 8 August 2016; Revised 6 December 2016; Accepted 5 January 2017; Published 31 January 2017

Academic Editor: Jussi Tohka

Copyright © 2017 Kuo-Wei Hsu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.

Author: Kuo-Wei Hsu

Source: https://www.hindawi.com/


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