边缘计算中的启发式自组织语言属性深度学习用于IoT智能
A Heuristically Self-Organised Linguistic Attribute Deep Learning in Edge Computing For IoT Intelligence
摘要：
随着物联网（IoT）的发展，物联网智能成为新兴技术。“非专业性的诅咒”是边缘设备中数据融合的障碍，是物联网智能成功的关键。嵌入有语言决策树（LDT）的语言属性层次（LAH）可以表示新的属性深度学习。与传统的深度学习相比，LAH可以通过LAH中的LDT产生的规则提供透明的信息传播来克服缺失解释的缺点。与传统的深度学习类似，优化LAH的计算复杂度阻碍了LAH的应用。在本文中，我们提出了一种启发式的方法来构建一个LAH，嵌入LDTs的决策或分类，利用属性之间的距离相关性和属性和目标变量之间。该方法首先将属性集划分为若干个属性簇，然后对这些属性簇进行语义组织，形成语言属性层次结构。所提出的方法进行了验证，一些基准决策或分类问题，从UCI机器学习知识库。实验结果表明，该自组织算法能够有效地构造出一个语言属性层次结构。这种嵌入LDT的自组织语言属性层次结构不仅可以有效地解决单个LDT中的“维数灾难”，用于具有海量属性的数据融合，而且与单个LDT相比，可以在决策或分类方面实现更好或相当的性能。自组织算法比Wrapper中的遗传算法更有效。这使得将自组织算法嵌入边缘设备以实现物联网智能变得可行。
英文摘要：
With the development of Internet of Things (IoT), IoT intelligence becomes emerging technology. "Curse of Dimensionality" is the barrier of data fusion in edge devices for the success of IoT intelligence. A Linguistic Attribute Hierarchy (LAH), embedded with Linguistic Decision Trees (LDTs), can represent a new attribute deep learning. In contrast to the conventional deep learning, an LAH could overcome the shortcoming of missing interpretation by providing transparent information propagation through the rules, produced by LDTs in the LAH. Similar to the conventional deep learning, the computing complexity of optimising LAHs blocks the applications of LAHs. In this paper, we propose a heuristic approach to constructing an LAH, embedded with LDTs for decision making or classification by utilising the distance correlations between attributes and between attributes and the goal variable. The set of attributes is divided to some attribute clusters, and then they are heuristically organised to form a linguistic attribute hierarchy. The proposed approach was validated with some benchmark decision making or classification problems from the UCI machine learning repository. The experimental results show that the proposed self-organisation algorithm can construct an effective and efficient linguistic attribute hierarchy. Such a self-organised linguistic attribute hierarchy embedded with LDTs can not only efficiently tackle "curse of dimensionality" in a single LDT for data fusion with massive attributes, but also achieve better or comparable performance on decision making or classification, compared to the single LDT for the problem to be solved. The self-organisation algorithm is much efficient than the Genetic Algorithm in Wrapper for the optimisation of LAHs. This makes it feasible to embed the self-organisation algorithm in edge devices for IoT intelligence.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
