A hybrid approach for cancer classification based on particle swarm optimization and prior information

Abstract

In this paper, an improved method for cancer classification based on particle swarm optimization (PSO) and prior information is proposed. Firstly, the proposed method uses PSO to implement gene selection. Then, the global search algorithm such as PSO is combined with the local search one such as backpropagation (BP) to model the classifier. Moreover, the prior information extracted from the data is encoded in PSO for better performance. The proposed approach is validated on two publicly available microarray data sets. The experimental results verify that the proposed method selects fewer discriminative genes with comparable performance to the traditional classification approaches.