Feature Selection for Cancer Classification Based on SRPSO Algorithm

摘要：

　　Improving the accuracy of cancer classification plays an important role in cancer-assisted diagnosis.Genes selection is an important factor for improving the accuracy of cancer classification.In this paper,based on the standard particle swarm optimization algorithm,an SRPSO algorithm with self-adaptive and reverse-learning mechanism is proposed.It is applied to select feature genes from microarray datasets,and the results are used for cancer classification via SVM to make 5-fold cross-validation.To evaluate the performance of SRPSO,four different cancer datasets including Colon,ALL_AML,MLL,and SRBCT were selected.Based on the evaluation process,the SRPSO algorithm provided better results on each dataset.

关键词： Microarray gene data Cancer classification Particle swarm optimization algorithm Self-adaptive Reverse-learning

作者: Qiu-lan XIAO Hong ZHENG Qing-an YAO

作者单位: School of Computer Science and Engineering,Changchun University of Technology,Changchun,Peoples Republic of China

会议类型: 国际会议

会议名称: 2019 International Conference on Informatics, Control and Robotics 2019信息学、控制和机器人学国际会议(ICICR2019)

会议地点: 上海

会议语种:英文

页码: 238-243

在线出版日期: 2019-06-16（万方平台首次上网日期，不代表论文的发表时间）