运动数据中的最优关联数据的分类分析

Classification analysis of optimal association data in motion data

针对传统的分类方法在对运动数据的最优关联数据进行分类时,存在分类误差大、效率低的问题,提出基于决策树寻优的运动数据中的最优关联数据的分类算法。采用有向图模型构建运动数据的最优关联数据分布式结构模型,对采集的运动数据进行非线性时间序列特征重构,在重构的运动数据高维特征空间中进行运动数据的关联性映射,提取反映运动数据类别的属性特征,通过决策树寻优方法实现运动数据的自适应分类。仿真结果表明,采用该方法进行运动数据中的最优关联数据分类,其准确性较好、误分率较低,从而有效挖掘运动数据的关联信息,实现运动特征监测。

In allusion to the problem that big error and low efficiency exist in the traditional classification method of opti- mal association data classification for motion data, the optimal association data classification algorithm based on decision tree op- timization is proposed for motion data. The directed graph model is adopted to construct the optimal association data distributed structure model for motion data. The nonlinear time series feature is reconstructed for the collected motion data. Association map- ping of motion data is performed in the reconstructed high-dimensional feature space of motion data to extract the attribute fea- tures reflecting motion data categories. The decision tree optimization method is adopted to realize adaptive motion data classifica- tion. The simulation results show that the method has good accuracy and low error in optimal association data classification for motion data, and can perform effective association information mining for motion data to realize motion feature monitoring.