基于深度神经决策森林的体域网数据融合方法

Body area network data fusion method based on deep neural decision forests

针对体域网的多传感器数据采集过程中存在的数据冗余大、特征信息模糊问题,提出了一种基于深度神经决策森林(DNDF)的数据融合方法。首先根据目标任务的实际需求,使用卷积神经网络进行相关特征提取,再将决策树放置到全连接层之后进行精细化数据分类。通过使用DNDF方法,不仅能够有效提取多维数据的关键特征,而且能够较好地兼顾数据间的关联性。实验以ARe M数据集作为实验样本,结果表明,DNDF方法相对其他传统算法具有更好的分类准确率,分类准确率达到了96.5%。

Aiming at the problems of large data redundancy and unclear characteristics of multi-sensor data acquisition in the body area network,this paper proposed a data fusion method based on deep neural decision forests(DNDF).The method used a convolutional neural network for feature extraction,and classified the data after placing the decision tree into the fully connected layer.The DNDF method could extract the critical features of the multi-dimensional data effectively,and preserved the correlation between the data better.The experiment used the AReM dataset.The results show that the DNDF method has better performance than other traditional algorithms and achieves higher classification accuracy.