论文标题
物联网数据分类的机器和深度学习算法的性能分析和比较
Performance Analysis and Comparison of Machine and Deep Learning Algorithms for IoT Data Classification
论文作者
论文摘要
近年来，物联网（IoT）作为新兴技术的增长令人难以置信。物联网域中的网络Enabled设备的数量正在急剧增加，从而导致电子数据的大量生产。这些数据包含有价值的信息，这些信息可在各个领域（例如科学，工业，商业甚至社交生活）中使用。为了提取和分析这些信息并使物联网系统聪明，唯一的选择就是进入人工智能（AI）世界，并利用机器学习和深度学习技术的力量。本文使用六个与IOT相关的数据集评估了11台流行的机器和深度学习算法的性能。根据几个性能评估指标进行比较这些算法，包括精度，召回，F1得分，准确性，执行时间，ROC-AUC得分和混淆矩阵。还进行了特定的实验，以评估开发模型的收敛速度。全面的实验表明，考虑到所有性能指标，随机森林的性能要比其他机器学习模型更好，而在深度学习模型中，Ann和CNN取得了更有趣的结果。
In recent years, the growth of Internet of Things (IoT) as an emerging technology has been unbelievable. The number of networkenabled devices in IoT domains is increasing dramatically, leading to the massive production of electronic data. These data contain valuable information which can be used in various areas, such as science, industry, business and even social life. To extract and analyze this information and make IoT systems smart, the only choice is entering artificial intelligence (AI) world and leveraging the power of machine learning and deep learning techniques. This paper evaluates the performance of 11 popular machine and deep learning algorithms for classification task using six IoT-related datasets. These algorithms are compared according to several performance evaluation metrics including precision, recall, f1-score, accuracy, execution time, ROC-AUC score and confusion matrix. A specific experiment is also conducted to assess the convergence speed of developed models. The comprehensive experiments indicated that, considering all performance metrics, Random Forests performed better than other machine learning models, while among deep learning models, ANN and CNN achieved more interesting results.
