计算机工程2019，Vol.45Issue(2)：278-283,6.DOI:10.19678/j.issn.1000-3428.0049387
基于PSO混合特征选择算法在疲劳驾驶中的应用
Application of Hybrid Feature Selection Algorithm Based on Particle Swarm Optimization in Fatigue Driving
摘要
Abstract
There are some problems in driver fatigue detection research based on multi-source physiological signals, such as the redundancy of characteristic information and the influence of wearing multiple sensors on driver operation.Therefore, a feature selection algorithm combining Particle Swarm Optimization (PSO) algorithm and Sequential Backward Selection (SBS) is proposed. The penalty term of signal source number is added to the fitness function to reduce the number of sensors while reducing the feature dimension. According to the characteristics of the classifier used, the fitness function is simplified and the efficiency of the feature selection algorithm is improved. The signal selection bit is added to the definition of particle to improve the signal screening. Experimental results show that this algorithm uses an average of 2 signals and 16. 1 features, and can achieve an accuracy of 95. 3％ in fatigue driving detection.关键词
疲劳驾驶/多源生理信号/混合特征选择/粒子群优化/序列后向选择Key words
fatigue driving/multi-source physiological signals/hybrid feature selection/Particle Swarm Optimization (PSO)/Sequential Backward Selection (SBS)分类
医药卫生引用本文复制引用
林雨培,陈兰岚,邹俊忠..基于PSO混合特征选择算法在疲劳驾驶中的应用[J].计算机工程,2019,45(2):278-283,6.基金项目
国家自然科学基金 (61201124) （61201124）
中央高校基本科研业务费重点科研基地创新基金 (222201717006). （222201717006）
