摘要
让计算机具有识别情感的能力是情感智能的主要标志和实现高级别人机交互的重要前提,其中通过记录和分析生理信号来识别情感状态已经成为情感计算和人机交互研究领域中的热点。针对多生理信号情感识别过程中的特征冗余以及在大样本数据下传统特征降维算法效率普遍不高的现状,提出了结合模拟退火和粒子群算法的混合智能优化算法(SA-PSO)来解决情感特征选择的问题,并结合带权重的离散KNN分类算法(WD-KNN),充分利用情感样本信息进行特征分类。通过对实验仿真数据的分析和与其他方法识别结果的比对,提高了识别率和效率,验证了算法的有效性。
Developing a machine＇s ability to recognize emotion states is one of the hallmarks of emotional intelligence and important prerequisite for high-level human computer interaction （HCI）. Recording and recognizing physiological signals of emotion has become an increasingly important field of research in affective computing and HCI. For the problem of feature redundancy of physiological signals-based emotion recognition and low efficiency of traditional feature reduction algorithms on great sample data,a hybrid intelligent optimization algorithm based on the simulated annealing algorithm and particle swarm optimization algorithm （SA-PSO）was proposed to solve the problem of emotion feature selection. Then a weighted discrete-KNN classifier（WD-KNN）was presented to classify features by making full use of emotion sample information. The recognition rate and efficiency was increased and the algorithm＇s validity was verified through the analysis of experimental simulation data and the comparison of several recognition methods.
出处
《电信科学》
北大核心
2010年第9期129-135,共7页
Telecommunications Science
基金
国家自然科学基金重点资助项目(No.60533080)
关键词
情感计算
情感识别
特征选择
混合智能优化
affective computing, emotion recognition, feature selection, hybrid intelligent optimization
