Structure Learning Algorithm for DBN Transition Networks Based on Ant Colony Optimization
Ren-Bing HU, Junzhong Ji, Hong-Xun ZHANG, Chunnian Liu · Jisuanji gongcheng · 2009
Aiming at the characteristics of dynamic Bayesian transition networks, this paper proposes a structure learning algorithm based on Ant Colony Optimization(ACO) named ACO-DBN-2S by extending the static Bayesian networks structure leaning algorithm I-ACO-B. In ACO-DBN-2S, ants select arcs from the inter-arcs between time slices before from the intra-arcs in one slice, and the interval optimization strategy is improved by decreasing the times of optimization operation. A number of experiments under standard datasets demonstrate the algorithm can handle large data, and the precision and speed of learning are improved.