自动化学报2025，Vol.51Issue(7)：1546-1561,16.DOI:10.16383/j.aas.c240599
多标签情感计算中的TSK模糊系统与域适应方法研究
A Study of TSK Fuzzy System and Domain Adaptation Method in Multi-label Affective Computing
摘要
Abstract
Affective computing,as an important branch of human-computer interaction(HCI),is a key guarantee for realizing a harmonious and natural HCI experience.How to utilize easily accessible physiological signals for ac-curate emotion recognition has become a hot topic.The widely used emotion model usually describes emotions from multiple dimensions,such as pleasure,arousal,and dominance,etc.However,most of the existing emotion recogni-tion methods consider different dimensions separately,ignore the correlation relationship between dimensions,and have limitations in interpretability.Although the multi-label TSK fuzzy system can compensate for the above short-comings,it still faces the problems of difficulty in constructing fuzzy rules and low training efficiency under high-di-mensional input.In addition,multimodal physiological signals have large individual variability,which seriously af-fects the accuracy of cross-user emotion recognition.In this paper,we firstly propose a multi-label TSK fuzzy sys-tem with rule dimensionality reduction(RDR-MLTSK)to optimize the fuzzy system structure and the training effi-ciency;Furthermore,we propose a multi-label fuzzy domain adaptation algorithm(MLFDA)to achieve multi-source domain migration learning,which improves the generalization performance of RDR-MLTSK.The experimental res-ults on two publicly available datasets,DEAP and DECAF,show that the proposed methods can effectively im-prove the accuracy of emotion recognition and has better performance compared to classical and advanced methods.关键词
情感计算/多标签学习/TSK模糊系统/领域自适应Key words
Affective computing/multi-label learning/TSK fuzzy system/domain adaptation引用本文复制引用
何欣润,李毅轩,傅中正,伍冬睿,黄剑..多标签情感计算中的TSK模糊系统与域适应方法研究[J].自动化学报,2025,51(7):1546-1561,16.基金项目
国家重点研发计划(2022YFB4700200),国家自然科学基金(62333007),深圳市科技计划(JCYJ20220818103602004)资助Supported by National Key Research and Development Pro-gram of China(2022YFB4700200),National Natural Science Foundation of China(62333007),and Shenzhen Science and Technology Program(JCYJ20220818103602004) （2022YFB4700200）
