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題名 A theoretical analysis of why hybrid ensembles work
作者 Hsu, Kuo-Wei
徐國偉
貢獻者 資訊科學系
關鍵詞 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
日期 2017
上傳時間 27-Jul-2017 12:52:03 (UTC+8)
摘要 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.
關聯 Computational Intelligence and Neuroscience, Volume 2017 (2017), Article ID 1930702, 12 pages
資料類型 article
DOI http://dx.doi.org/10.1155/2017/1930702
dc.contributor 資訊科學系
dc.creator (作者) Hsu, Kuo-Weien-US
dc.creator (作者) 徐國偉zh-tw
dc.date (日期) 2017
dc.date.accessioned 27-Jul-2017 12:52:03 (UTC+8)-
dc.date.available 27-Jul-2017 12:52:03 (UTC+8)-
dc.date.issued (上傳時間) 27-Jul-2017 12:52:03 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/111424-
dc.description.abstract (摘要) 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.
dc.format.extent 1626725 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Computational Intelligence and Neuroscience, Volume 2017 (2017), Article ID 1930702, 12 pages
dc.subject (關鍵詞) 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
dc.title (題名) A theoretical analysis of why hybrid ensembles worken-US
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1155/2017/1930702
dc.doi.uri (DOI) http://dx.doi.org/10.1155/2017/1930702