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.
Bibliographical noteFunding 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).
© 2015, Springer-Verlag Berlin Heidelberg.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Geometry and Topology