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Soft Computing

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20.08.2015 | Methodologies and Application

Ensemble bayesian networks evolved with speciation for high-performance prediction in data mining

verfasst von: Kyung-Joong Kim, Sung-Bae Cho

Erschienen in: Soft Computing | Ausgabe 4/2017

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Abstract

Bayesian networks (BNs) can be easily refined (or learn) using data given prior knowledge about a changing environment. Furthermore, by exploring multiple diverse BNs in parallel, it is expected that an intelligent system may adapt quickly to changes in the environment, resulting in robust prediction. Recently, there have been attempts to design BN structures using evolutionary algorithms; however, most of these have used only the fittest solution from the final generation. Because it is difficult to combine all of the important factors into a single evaluation function, the solution is often biased and of limited adaptability. Here we describe a method of generating diverse BN structures via speciation and selective combination for adaptive prediction. Experiments using the seven benchmark networks show that the proposed method can result in improved accuracy in handling uncertainty by exploiting ensembles of BNs evolved by speciation.

Vorheriger Artikel Power series methods of summability for series of fuzzy numbers and related Tauberian Theorems

Nächster Artikel Uncertainty measures of rough sets based on discernibility capability in information systems

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Titel: Ensemble bayesian networks evolved with speciation for high-performance prediction in data mining
verfasst von: Kyung-Joong Kim
Sung-Bae Cho
Publikationsdatum: 20.08.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 4/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-015-1841-z

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