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Abstract：
Sensor Data Fusion (SDT) algorithms and models have been widely used in diverse applications. One of the main challenges of SDT includes how to deal with heterogeneous and complex datasets with different formats. The present work utilised both homogenous and heterogeneous datasets to propose a novel SDT framework. It compares data mining-based fusion software packages such as RapidMiner Studio, Anaconda, Weka, and Orange, and proposes a data fusion framework suitable for in-home applications. A total of 574 privacy-friendly (binary) images and 1722 datasets gleaned from thermal and Radar sensing solutions, respectively, were fused using the software packages on instances of homogeneous and heterogeneous data aggregation. Experimental results indicated that the proposed fusion framework achieved an average Classification Accuracy of 84.7% and 95.7% on homogeneous and heterogeneous datasets, respectively, with the help of data mining and machine learning models such as Naïve Bayes, Decision Tree, Neural Network, Random Forest, Stochastic Gradient Descent, Support Vector Machine, and CN2 Induction. Further evaluation of the Sensor Data Fusion framework based on cross-validation of features indicated average values of 94.4% for Classification Accuracy, 95.7% for Precision, and 96.4% for Recall. The novelty of the proposed framework includes cost and timesaving advantages for data labelling and preparation, and feature extraction.
摘要：
传感器数据融合（SDT）算法和模型已广泛应用于各种应用中。SDT的主要挑战之一包括如何处理具有不同格式的异构和复杂数据集。目前的工作利用同质和异构数据集提出了一个新的SDT框架。它比较了基于数据挖掘的融合软件包，如RapidMinerStudio，蟒蛇,Weka,橙色,并提出了一种适用于家庭应用的数据融合框架。从热和雷达传感解决方案中收集的574张隐私友好（二进制）图像和1722个数据集，分别,在同构和异构数据聚合的实例上使用软件包进行融合。实验结果表明，所提出的融合框架在同质和异构数据集上的平均分类精度分别为84.7%和95.7%。分别,在数据挖掘和机器学习模型的帮助下，如朴素贝叶斯，决策树,神经网络,随机森林,随机梯度下降，支持向量机,和CN2诱导。基于特征交叉验证的传感器数据融合框架的进一步评估表明，分类精度的平均值为94.4%，精度为95.7％，召回率为96.4%。拟议框架的新颖性包括数据标签和准备的成本和时间节省优势，和特征提取。
