A new Bayesian network (BN) learning method using a hybrid algorithm and chaos theory is proposed. The principles of mutation and crossover in genetic algorithm and the cloud-based adaptive inertia weight were incorporated into the proposed simple particle swarm optimization (sPSO) algorithm to achieve better diversity, and improve the convergence speed. By means of ergodicity and randomicity of chaos algorithm, the initial network structure population is generated by using chaotic mapping with uniform search under structure constraints. When the algorithm converges to a local minimal, a chaotic searching is started to skip the local minima and to identify a potentially better network structure. The experiment results show that this algorithm can be effectively used for BN structure learning.

Date Published: 17 May 2012
PDF: 12 pages
Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 839216 (17 May 2012); doi: 10.1117/12.920302

Published in SPIE Proceedings Vol. 8392:
Signal Processing, Sensor Fusion, and Target Recognition XXI
Ivan Kadar, Editor(s)