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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/111424

Title:	A theoretical analysis of why hybrid ensembles work
Authors:	Hsu, Kuo-Wei 徐國偉
Contributors:	資訊科學系
Keywords:	Decision making;Decision trees;Trees (mathematics);Bayes classification;Classification algorithm;Classification performance;Data mining algorithm;Ensembles of classifiers;Group decision making process;Theoretical foundations;Data mining;algorithm;artificial intelligence;automated pattern recognition;Bayes theorem;human;Algorithms;Artificial Intelligence;Bayes Theorem;Humans;Pattern Recognition, Automated
Date:	2017
Issue Date:	2017-07-27 12:52:03 (UTC+8)
Abstract:	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. © 2017 Kuo-Wei Hsu.
Relation:	Computational Intelligence and Neuroscience, Volume 2017 (2017), Article ID 1930702, 12 pages
Data Type:	article
DOI 連結:	http://dx.doi.org/10.1155/2017/1930702
DOI:	10.1155/2017/1930702
Appears in Collections:	[心理學系] 期刊論文

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