论文标题
启发式自我组织的语言属性在物联网智能中的边缘计算中的深度学习
A Heuristically Self-Organised Linguistic Attribute Deep Learning in Edge Computing For IoT Intelligence
论文作者
论文摘要
随着物联网（IoT）的发展，物联网智能成为新兴技术。 “维度的诅咒”是边缘设备中数据融合的障碍，以实现物联网智能的成功。语言属性层次结构（LAH）嵌入了语言决策树（LDTS），可以代表一种新的属性深度学习。与传统的深度学习相反，LAH可以通过通过LDT在LAH中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的计算复杂性阻止了LAH的应用。在本文中，我们提出了一种启发式方法来构建LAH，并通过利用属性之间的距离相关性，属性与目标变量之间的距离相关性来嵌入LDT进行决策或分类。一组属性分为某些属性簇，然后将它们启发性地组织起来形成语言属性层次结构。通过从UCI机器学习存储库中的一些基准决策或分类问题来验证了所提出的方法。实验结果表明，提出的自组织算法可以构建有效，有效的语言属性层次结构。与LDT嵌入的这种自组织的语言属性层次结构不仅可以在单个LDT中有效地处理“维度的诅咒”，以使数据融合具有大量属性，而且还可以在决策或分类方面取得更好或可比性的性能，而与单个LDT相比，要解决的问题。自组织算法比包装器中的遗传算法效率要高得多，以优化LAH。这使得将自组织算法嵌入到物联网智能的边缘设备中是可行的。
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.
