Network structural optimization based on swarm intelligence for highlevel classification
While most part of the complex network models are described in function of some growth mechanism, the optimization of a goal or certain characteristics can be desirable for some problems. This paper investigates structural optimization of networks in the highlevel classification context, where the classification produced by a traditional classifier is combined with the classification provided by complex network measures. Using the recently proposed social learning particle swarm optimization (SL PSO) a bio inspired optimization framework, which is responsible to build up the network and adjust the parameters of the hybrid model while conducting the optimization of a quality function, is proposed. Experiments on two real world problems, the Handwritten Digits Recognition and the Semantic Role Labeling (SRL) were performed. In both problems, the optimization framework is able to improve the classification given by a state of the art algorithm to SRL. Furthermore, the optimization framework proposed here can be extended to other machine learning tasks.
2016 International Joint Conference on Neural Networks (IJCNN)