Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

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

Ensemble bayesian networks evolved with speciation for high-performance prediction in data mining 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Soft Computing Springer Journals

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

Soft Computing , Volume 21 (4) – Aug 20, 2015

Loading next page...
 
/lp/springer-journals/ensemble-bayesian-networks-evolved-with-speciation-for-high-fJhnH65yY8

References (48)

Publisher
Springer Journals
Copyright
Copyright © 2015 by Springer-Verlag Berlin Heidelberg
Subject
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Mathematical Logic and Foundations; Control, Robotics, Mechatronics
ISSN
1432-7643
eISSN
1433-7479
DOI
10.1007/s00500-015-1841-z
Publisher site
See Article on Publisher Site

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.

Journal

Soft ComputingSpringer Journals

Published: Aug 20, 2015

There are no references for this article.