Software refactoring scheme optimization model based on set pair analysis
基于集对分析的软件重构方案优选模型

Used the video clips with emotional colors as the inducing materials, and collected GSR signals as database for emotion recognition, studied then the effect of immune operations on feature selection. Firstly, extracted 30 statistic features from GSR signals, and normalized by the values of corresponding features under emotion calm. Then through adding immune operations to HPSO, presented IH-PSO for feature selection. Adopted Fisher classifier to test the selection effect. Finally, used both of the selected feature combinations caught by the two algorithms for emotion identification and verification. The verification results show that compared with HPSO, IH-PSO can obtain higher recognition rate with fewer features. All those illustrate that the application of the immune system can earn much better feature selection effect.