肿瘤亚型识别研究中智能算法的应用.

Objective:In order to solve the dimension disaster and over-fitting problems in the process of tumor subtype recognition, a particle swarm optimization (PSO) BP neural network ensemble algorithm was proposed.Methods:The Euclidean distance and mutual information was used to preliminarily filter redundant genes, and then Relief algorithm was adopted to further process the candidate feature genes set. The BP neural network was used as the base classifier, which combines feature genes extraction with classifier training.Results:When the number of hidden layer neurons is 5 and the number of candidate feature genes is 110, the QPSO/BP algorithm can optimize and search globally.Conclusion:The algorithm not only improves the accuracy of tumor classification and recognition, but also reduces the complexity of learning. [ABSTRACT FROM AUTHOR]