基于邻域一致性和DBPSO的跌倒检测特征集优化算法

摘要

目前国际上没有标准的、权威的老人跌倒检测数据,并且由年轻人模仿跌倒得到的样本规模较小,因此,如何利用有限的数据集找到最具代表性的特征集就显得尤为重要.考虑到特征集具有数值型和低样本数的特点,提出一种基于邻域一致性指标和离散二进制粒子群的特征集组合优化算法.该算法首先利用启发式快速向前算法,通过优化的邻域一致性指标来构成初选特征集;然后采用离散二进制粒子群算法进行有效寻优,剔除冗余特征;最后利用分类算法以验证该算法的有效性.实验结果表明,该算法可以获得具有较少特征又有较强分类能力的特征子集,并且算法的效率也得到了提高.%At present there is no standard,authoritative fall detection test data,and the sample size by young people imitating fall is small,so how to use a limited data set to find the most representative feature set is particularly important.According to the characteristics of feature set in low sample and continuous type,a feature set optimization algorithm based on neighborhood consistency and discrete binary particle swarm optimization (DBPSO) was proposed.The algorithm firstly constituted the primary feature set based on optimized neighborhood consistency function and heuristic forward searching algorithm,and then used the primary feature set to initialize the population of DBPSO.At last the validity of the algorithm was verified using classification algorithm.The experimental results show that the algorithm can improve classification ability with fewer features selected,and the computational efficiency is also improved.