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

Kyung Joong Kim, Sung Bae Cho

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1065-1080
Number of pages16
JournalSoft Computing
Issue number4
Publication statusPublished - 2017 Feb 1

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIP) (2013 R1A2A2A01016589) and the Industrial Strategic Technology Development Program, 10044828, Development of augmenting multisensory technology for enhancing significant effect on service industry, funded by the Ministry of Trade, Industry & Energy (MI, Korea).

Publisher Copyright:
© 2015, Springer-Verlag Berlin Heidelberg.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Software
  • Geometry and Topology


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